Research Studentships

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Funding Your Research

At the University of Lincoln, postgraduate students are an integral part of our research community. They work alongside talented academics and researchers from around the world, contributing to our growing reputation for internationally excellent research.

There are opportunites to get involved in exciting research projects by applying for a studentship. The University offers a range of studentships including funded and part-funded opportunities, please refer to the current studentships information below.  

CDT 2 Col

EPSRC Centre for Doctoral Training

The University of Lincoln is launching the world's first Centre for Doctoral Training in Agri-Food Robotics in collaboration with the University of Cambridge and the University of East Anglia. This new advanced training centre in agri-food robotics will create the largest ever cohort of Robotics and Autonomous Systems (RAS) specialists for the global food and farming sectors, thanks to a multi-million pound funding award the Engineering and Physical Sciences Research Council (EPSRC).

Applications are now open for entry into the CDT programme, starting in September 2020.

Find out More

Current Studentship Opportunities

Use the dropdown menus below to browse current funded and part-funded studentship opportunities at the University of Lincoln, listed by academic College. 

Studentship Terms and Conditions

College of Arts

AHRC Collaborative Doctoral Partnership (CDP) studentship – Women collectors of South Asia: gender, material culture, and empire

Start date: 1 October 2020

Application Deadline: Tuesday 9 June

Interviews are likely to take place on Friday 26 June at the British Museum if permitted by then, otherwise online.

The University of Lincoln and the British Museum are pleased to announce the availability of a fully-funded Collaborative doctoral studentship from October 2020 under the AHRC’s Collaborative Doctoral Partnership Scheme. We encourage applications from suitable candidates with relevant experience, as well as coming from an academic background. This studentship can be studied full or part-time.

This project explores the critical role that women played in collecting objects from South Asia during the colonial and post-colonial eras, highlighting the agency of women of all backgrounds in the formation of museum collections and knowledge production.

This project will be jointly supervised by Dr Sarah Longair and Dr Sushma Jansari and the student will be expected to spend time at both the University of Lincoln and the British Museum, as well as becoming part of the wider cohort of CDP funded students across the UK.

Project Overview 

This project will investigate the lives, collections, activities, writings, and networks of women collectors and donors of material culture from South Asia to the British Museum and other collections in the UK. It will explore how and why women collected objects and what motivated them, how they acquired knowledge about their collections and the dynamics of their donation of objects to museums.

With a focus on objects from South Asia, this project will be set in a colonial and/or post-colonial context, during which South Asian, British, and later British South Asian women negotiated the challenges of living under the British Empire and its aftermath. It will make an important and original contribution to our understanding of the British Museum as well as other institutions, demonstrating how gender, race, and empire influenced the forging of collections. It will examine how far collecting and engagement with material culture was a means for women to establish their own network, and how these factors aligned with or challenged racial and social divides in imperial and post-imperial settings.

Using the British Museum as a starting point, it will trace the lives of selected women and their collections and donations to the British Museum and other UK museums. Objects themselves will be studied as well as associated archival material. Personal papers and publications will shed further light on the way in which women collected and donated, the networks within which they acted, intermediaries who assisted them, and how this knowledge was disseminated. The research will require the student to spend time researching at the British Museum, along with other museums and archives.

Research questions include:

  • Which women collected in South Asia and what was their role in colonial/post-colonial society?
  • Where and how did they acquire objects, and what motivated them?
  • What informed the decisions of women vendors and donors when passing on collections to museums, and what tensions existed in this process?
  • How distinctive were their collecting patterns – i.e. are their collections representative or exceptional?

Details of Award

CDP doctoral training grants fund full-time studentships for 45 months (3.75 years) or part-time equivalent. The studentship has the possibility of being extended for an additional 3 months to provide professional development opportunities, or up to 3 months of funding may be used to pay for the costs the student might incur in taking up professional development opportunities.      

The award pays tuition fees up to the value of the full-time home/EU UKRI rate for PhD degrees. Research Councils UK Indicative Fee Level for 2020/21 is £4,407.

The award pays full maintenance for UK citizens and residents only. The Doctoral Stipend for 2020/21 for this award is £16,885 per year. In addition, the British Museum will cover research expenses up to £1,000 per year. 

Further details can be found on the UKRI website:

The project can be undertaken on a full-time or part-time basis.


  • We want to encourage the widest range of potential students to study for a CDP studentship and are committed to welcoming students from different backgrounds to apply.
  • Applicants should ideally have or expect to receive a relevant Master's-level qualification, or be able to demonstrate equivalent experience in a professional setting. Suitable disciplines are flexible, but might include History, Anthropology, Archaeology, Art History, or Museum Studies.
  • Applicants must be able to demonstrate an interest in the museum sector and potential and enthusiasm for developing skills more widely in related areas.
  • As a collaborative award, students will be expected to spend time at both the University of Lincoln and the British Museum

All applicants must meet the AHRC’s academic criteria and residency requirements:

How to Apply

To apply for this studentship, please follow the link and prepare the following documents:

  • CV (maximum 2 sides A4)
  • Covering letter describing your reasons for wishing to undertake this project, how your prior experience prepares you for this studentship, how you would refine the project based on your interests and experience, and a proposed research plan (maximum 3 sides of A4).

Those called for interview will be asked to prepare a presentation.

Apply Now

Please arrange for two references to be sent directly to

The successful candidate will be eligible to participate in CDP Cohort Development events.

All new CDP students will be expected to attend the CDP Student Launch Event on Monday 21 September 2020 at the British Museum.


Please contact Dr Sarah Longair for informal questions about the studentship.

College of Science

EPSRC logo

EPSRC Doctoral Training Partnership (DTP) Studentships

The University of Lincoln has received funding from the Engineering and Physical Sciences research Council to establish a Doctoral Training Partnership (DTP), which will provide skills training to foster the next generation of world-class research leadership in areas of strategic importance to both EPSRC and the University of Lincoln.

Our training programme prioritises the following three thematic areas of robotics and artificial intelligence: smart energy; medical diagnosis and healthcare support systems; and bio-physics inspired robotics, in which the University has strong research groups. These research groups will provide DTP students with a rich research environment and a broad range of experienced and new researchers.

Each studentship will be associated with a specific project that will be designed to advance fundamental research in computer science or engineering within one of the thematic areas. Interdisciplinary links with other subject areas will also be expected.

We are currently providing the following 8 potential projects for the 2020-21 studentship application round and these are detailed below.

Each studentship covers 3 and half years of tuition fees at UK/EU student rates, a tax-free stipend at EPSRC rates, and a generous research training support grant enabling international travel and participation in the leading conferences and symposia.

Studentship applications are now open for entry into the DTP programme, starting in September 2020.

Please note: Due to funder restrictions, we are unable to accept applications from non-UK/EU applicants. Additionally due to current circumstances interviews may take place online.


Applicants must:

1. be a UK or EU citizen and

2. meet residence requirements, which are normally:

a) to be eligible for a full award a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship (with some further constraint regarding residence for education).

b) To be eligible for a fees only award, a student must be ordinarily resident in a member state of the EU, in the same way as UK students must be ordinarily resident in the UK. For further information regarding residence requirements, please see the regulations.

For full eligibility information, please see the Full UKRI Terms and Conditions (PDF)

Closing date: Midnight, 1 July 2020.

Application Form

Smart Energy

Directed Assembly of Organometallic Complexes as Novel Electronic Constructs

Academic Contact – Dr Louis Adriaenssens (, Senior Lecturer, School of Chemistry.

Providing next-generation devices that satisfy society’s demands for a sustainable future presents a major challenge to the electronics industry. All conductive and semi-conductive materials rely on networks of overlapping atomic and molecular orbitals. The geometrical relationships between the atomic and molecular components of these materials defines orbital overlap and plays a major role in defining the material’s electronic properties and function. Understanding this relationship is a subject of intense current research.

In our groups, we employ organometallic complexes as the molecular building blocks for electronically-active materials. Organometallic complexes can be designed to stabilise either positive or negative charges. Through intermolecular overlap of ligand π orbitals, this charge can be spread through systems of neighbouring organometallic complexes, creating efficient routes for charge transport (i.e., electrons or holes).

To engineer orbital overlap, we take advantage of self-assembly processes that create crystalline materials featuring defined architectures of closely-packed organometallic complexes. These architectures delineate regular and precise intermolecular relationships that translate to defined routes for charge transport.

You will work with a skilled interdisciplinary supervisory team (synthetic, physical, computational) to design and create these materials from the bottom up. To do this you will synthesise organometallic building blocks that you pre-program to self-assemble into electronically active crystals. According to your assembly instructions, these materials will comprise defined architectures of π systems that provide a route for electron transport, thereby governing electronic and photovoltaic properties.

In the Molecular Materials Group at the University of York (supervised by Dr Alyssa-Jennifer Avestro), you will build devices that incorporate the crystalline materials you create. Through these devices you will probe the fundamental electronic properties (conductivity, mobility, etc) of your materials. You will use this data to relate architecture to electronic function and build a fundamental molecular-level and large-scale picture of how your materials work.

Throughout the project you will be guided by a computational model (quantum chemical molecular dynamics) you develop that explains and predicts the electronic behaviour of your materials (supervised by Dr Matt Watkins). You will use this model to design optimised 2nd generation materials for use in devices like field effect transistors (FETs). You will synthesis these materials and build and test these devices, placing your stamp on the field of molecular electronics.

Skills the student can learn:

The student can become versed in organic and organometallic synthesis including Schlenk line and glovebox techniques. They will have the chance to develop skill in characterisation techniques including, but not limited to, advanced NMR spectroscopy, mass spectroscopy, and X-ray diffraction. At York, the student can gain exposure to thin-film electronic device construction, conductivity analysis of solid-state samples, and advanced electron microscopies.

Ideal Candidates:

  • should have, at a minimum, a 2.1 degree in chemistry or a related discipline
  • can demonstrate skills and experience (or an aptitude for mastering) the synthetic and computational components of chemistry
  • are academically curious and think deeply and creatively
  • communicate well in both written and spoken English
  • are empathetic, kind, have great social skills, and enjoy working with others from diverse backgrounds
  • take responsibility for the progress and quality of projects


AI-based Multi-objective Decision Making for Efficient Energy Management of Smart Grids

Academic Contact: Dr Shouyong Jiang (, Lecturer in Machine Learning, School of Computer Science

With population growth and economic development, the total world energy consumption will increase 50% between 2018 and 2050, according to the US energy information administration (EIA). Great energy efficiency measures must be taken now to address the energy crisis and safeguard the future of energy. Smart grid is the enabling technology for this challenge as it allows two-way communication between energy suppliers and their customers, can automatically balance power supply and demand in the distribution grid, deliver a deeper insight into energy consumption and efficiently integrate renewable energy.

This project will focus on efficient AI-based energy management of smart grids to reduce energy costs and lower carbon footprint. In particular, it will investigate novel AI-based decision-making strategies trade-offing the profit of energy suppliers and the cost of energy users. This work involves computational modelling of smart grids in different scenarios and optimisation of energy management in each scenario. The project will develop an AI-based decision-making tool for smart grids that will be experimentally tested and analysed in order for academic and commercial use.

The successful candidate will work with the Machine Learning group at the School of Computer Science and School of Engineering at the University of Lincoln. This is an exciting opportunity for developing a career in AI for smart energy.

Specific requirements for candidates:

Interested applicants should hold, at a minimum, a 2.1 degree in AI, computer science, mathematics, engineering, or any other relevant discipline and are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest. They must evidence an ability to engage in scientific research and to work collaboratively as part of a team, must be able to carry out mathematic modelling for practical problems, and have a good knowledge of operational research and optimisation approaches, such as evolutionary computation, and multi-criteria decision making. They are expected to have good communication skills in written and spoken English in order to work with both computer scientists and engineers, to present research findings in workshops/conferences, and to publish papers in high-quality journals.

Medical Diagnosis and Healthcare Support Systems

AI Based Diagnosis and Support System for Cartilage Lesion Detection on Knee MRIs and Automated Rehabilitation Assessment with Quantitative Biomarkers

Academic Contact: Dr Lei Zhang (, Lecturer, School of Computer Science

This PhD studentship is focused on medical image analysis, computer vision, and intelligent data analysis, which will subsequently support clinical decisions for surgery and patients, creating significant potential impact. Primary tasks include developing novel AI algorithms and approaches, including (but not limited to) robust segmentation, 3D surface reconstruction, and feature extraction.

You will be expected to develop a fully automated AI based system that aims to detect cartilage lesions, automatically quantify the biomarkers based on the anatomic structures within the knee joint on MRIs, and an AI decision-making tool based on the data collected from smart sensors This area of research offers challenges such as; precise tissue segmentations within knee joint on MRIs, anomaly detection, and investigations into novel biomarkers generated from the data.

You will be given the opportunity to work across disciplines and engage with colleagues from the University of Lincoln and work with experts and clinicians in relevant hospitals.

Specific requirements for candidates:

The successful candidate should possess a 2:1 in Computer Science, Engineering, Physics, or a related discipline. Applicants with a relevant Master's are particularly welcome. Interested applicants are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest. E.g. experience in imaging analytics in relevant areas, including (but not limited to) (medical) image analysis, segmentation, surface reconstruction, and machine/deep learning, would be an advantage. They must evidence an ability to engage in scientific research and to work collaboratively as part of a team. Excellent communication skills in written and spoken English are also required.

Using Brain Computer Interfaces to understand distractions during Virtual Reality Tasks

Academic Contact: Dr Horia Alexandru Maior (, Lecturer, School of Computer Science

Attentional control is essential in order to focus on relevant information and fade out distracting events. The consequences range from reducing the enjoyment and quality of life to affecting the ability to concentrate at work or even causing accidents (e.g., while driving).

This ground-breaking project will use non-invasive and portable Brain Computer Interfaces (BCIs) (including functional Near Infrared Spectroscopy) and Applied Data Science to detect, learn more, and understand distractions during high and low perceptual load Virtual Reality tasks.

This exciting opportunity includes:

  • designing and conducting laboratory experiments which will include the use of BCIs
  • building and preparing study tasks (using Virtual Reality technologies)
  • processing and analysing data
  • writing scientific papers, posters, and presenting results.

By joining the Doctoral Training Partnership programme at the University of Lincoln, you will be working in a collaborative and stimulating environment, strengthened by cohort-driven activities, where knowledge-sharing and joint problem solving are the norm. The multidisciplinary nature of the programme will provide the opportunity to think about problems from a whole new perspective and explore innovative ideas. You will be part of a cross-discipline collaboration between Computer Science and Psychology, and join the Interactive Technologies Lab.

Skills and experience you can gain include:

  • opportunities to develop expertise in the use of brain and physiological data for research
  • quantitative research skills and applied statistics
  • develop critical thinking and problem solving
  • applied data science
  • develop strong verbal and written communication skills
  • opportunities to work alongside our partner Artinis (
  • opportunities to present research outputs at national and international venues.

Specific requirements for candidates:

Interested applicants should hold, at a minimum, a 2.1 degree in a relevant or related discipline and are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest. They must evidence an ability to engage in scientific research and to work collaboratively as part of a team. Excellent communication skills in written and spoken English are also required.

As this is a highly multi-disciplinary PhD opportunity, applicants from a wide range of backgrounds will be considered.

Solid Tumour Segmentation via Principal Axis Estimation Using Weakly Supervised Adversarial Deep Learning

Academic Contact: Dr James Brown (, Senior Lecturer, School of Computer Science. 

Segmentation is an essential part of many image-based diagnosis pipelines. Manual delineation of complex structures (e.g. solid tumours) remains the gold standard in many disciplines but is a highly labour intensive and meticulous task to perform in practice. Cross-sectional area measurements such as RECIST (Eisenhower et al. 2009) and RANO (Wen et al. 2010) are considerably less time consuming to perform than a complete segmentation, but still require comprehensive knowledge of tumour presentation and morphology. With the ever-increasing demand for AI-based approaches to perform segmentation automatically, there remains an unmet need for methods that can learn to harness such data.

This PhD project aims to develop a medical image segmentation approach based on weakly supervised deep learning. A convolutional neural network (CNN) will be developed to perform automatic measurements of a tumour’s principal axes, while also attempting to perform an accurate segmentation in the absence of paired ground truth labels. The method will be developed and validated using two publicly available tumour imaging datasets. The overall goal of this project is to produce a general-purpose segmentation approach for medical images that does not require the training data to be manually segmented, and instead relies on minimally labour-intensive annotations that are already collected as part of routine clinical care.

Specific requirements for candidates:

Interested applicants should carry, at a minimum, a 2:1 degree in a relevant or related discipline and are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest. They must evidence an ability to engage in scientific research and to work collaboratively as part of a team. Excellent communication skills in written and spoken English are also required.

Bio-physics Inspired Robotics

Collective Behaviour of Autonomous Organisms: From Bio-Particles to Robotics

Academic Contact: Dr Fabien Paillusson (, Senior Lecturer, School of Mathematics and Physics

Active Matter is an emerging interdisciplinary field in physics and applied mathematics which refers to systems comprising interacting agents which can drive their own motion (for instance birds, fish, insects, “smart” artificial micro-particles, or bio-mimicking robots). Active Matter systems are to be opposed to Inert Matter systems whose behaviours are entirely determined by the mechanical interactions between the agents. Consequently, in the past two decades Active Matter models have demonstrated complex collective behaviours such as the formation of active clusters, obstacle induced phase separation and organised flocking motions, which are usually not achievable in assemblies of inert agents.

These newly found “living structures” can in turn be implemented in real life with collections of bacteria, artificial micro-particles or bio-mimicking robots for industrial, medical, or military applications making use of their self-assembling properties and resilience to external influences.

Active Matter constitute promising systems in that simple sets of rules can lead to many rich phases of collective behaviours. There is ample opportunity to develop new classes of rules which can give rise to never-seen before phases and ultimately provide insights on how to reverse-engineer rules for targeted goals. This interdisciplinary project at the interface of physics, computer modelling and robotics will develop new theoretical and computational models for such systems and validate them on physical robotic swarms.

Specific Requirements for Candidates:

Interested applicants should carry, at a minimum, a 2.1 degree in either Physics, Mathematics, Engineering or other related discipline with good computational and communication skills. You must be motivated to learn new things and to work collaboratively as part of a team.

A Miniaturised Stiffness-controllable Soft Medical Manipulator

Academic Contacts: Dr Khaled Elgeneidy (, Lincoln Centre for Autonomous Systems and School of Engineering

Prof Mini Saaj, Lincoln Centre for Autonomous Systems and School of Engineering

Dr Fabien Paillusson, School of Mathematics and Physics

This PhD studentship will involve developing a miniaturised soft medical manipulator that utilises a novel controlled-stiffening mechanism to enhance tool stability and force output. The research will study particle jamming through bio-physical modelling and translating those concepts to design a bio-inspired medical robot that can actively change its stiffness in response to sensor data. Throughout this project, the candidate will work closely with clinical partners to guide the development of the soft manipulator.

The successful candidate will carry out research in the Bio-robotics and Medical Technologies Laboratory, within the School of Engineering at the University of Lincoln. There will be opportunities to work collaboratively with the Lincoln Centre for Autonomous Systems and Lincoln Medical School. Additionally, the project will be supported by an external clinical co-supervisor, Dr Mohamed Thaha, from St Bartholomew’s Hospital, Queen Mary University of London, as well as Dr Fulvio Forni, from University of Cambridge, who will act as an external academic co-supervisor.

We expect the successful student to develop their experience and several new skills during this PhD programme. These include, but are not limited to, the following:

  • in-depth knowledge of physical and mathematical modelling of flexible robots.
  • skills in creating Engineering drawings using CAD/ SolidEdge/Sketchup.
  • hands-on experience in building soft robots through rapid prototyping, system-level integration, hardware-in- the-loop testing, and ex-vivo testing using phantom organs.
  • training to advance technical writing skills for publishing articles in leading international journals and conferences.
  • networking skills through interacting with wider research groups, both internal and external, as well as excellent communication skills through presenting outputs at national and international forums (conferences/workshops).
  • teaching-related skills through in-lab demonstrations of robots for undergraduate and postgraduate students (optional).
  • Participation in public engagement events showcasing Robots (optional).

Specific requirements for candidates:

Interested applicants should hold, at a minimum, a 2.1 degree in a relevant engineering discipline (Robotics/Mechatronics/Mechanical) and are encouraged to demonstrate skills and/or experiences relevant to the project such as CAD, Matlab, Python/C++ programming, and ROS. Candidates should be able to demonstrate excellent problem-solving skills and ability to translate concepts to prototypes. They must evidence an ability to engage in scientific research and to work collaboratively as part of a team. Excellent communication skills in written and spoken English are also required.

Jumping-take off for Agricultural Drones

Academic Contacts: Dr Elisa Frasnelli (, Senior Lecturer, School of Life Sciences

Prof Gregory Sutton (, Royal Society University Research Fellow, School of Life Sciences

Prof Elizabeth Sklar (, Professor of Agri-Robotics and Research Director of Lincoln Agri-Robotics, Lincoln Institute for Agri-food Technology

This PhD studentship project involves investigation of jumping mechanisms as a means to deploy energy-efficient and accurate sensing robots in gardens and fields. The long term technical goal is to address challenges in obtaining complete data sets for visual inspection where aspects of the sensing environment are partially obstructed and the ability to strategically and dynamically move the camera will increase the possible viewpoint volume and, as a result, improve 3D models constructed from the data.

The focus for this project is on small unmanned aerial vehicles, or UAVs (sometimes referred to as "drones", though not the military fixed-wing style), in order to provide manoeuvrability within complex and variable spaces, concentrating on agricultural domains, though also applicable to many other domains. The project will entail design and analysis of various jumping and flying mechanisms, construction and evaluation of prototype devices in the lab, and experimental evaluation of successful prototypes with crops in test fields. Application of standard data science and machine learning techniques will be applied to aspects of experimental evaluation, with respect to assessing the improvement in data collection volume and quality obtained with different prototype device designs.

Our interest in this type of jump-start UAV is motivated by the need to provide high-precision sensing for monitoring crops. Insects thrive in gardens and fields, able to manoeuvre in these complex and variable settings, distinguish between different types of plants and recognise particular species that provide nutrition and shelter. Not just garden pests, insects also inspire us to design efficient sensing mechanisms. In order to practice precision agriculture (intelligent farming conducted at the level of individual plants, rather than whole fields), we need to detect features of plants growing closely together—a substantial challenge for traditional robotic sensing, which relies on large robots and cameras to gather broad images and employ machine learning methods to classify elements and distinguish individual plants from neighbours. If we were able to deploy small robots fitted with tiny sensors that could position themselves accurately alongside specific plants, then we would be solving two problems facing agricultural roboticists today: precisely locating individual plants and repeatedly gathering sensor data on the same plant.

Three sets of experiments will be conducted. First, lab-based comparisons of initial device prototypes with respect to energy usage for take-off, distance achieved, and position accuracy will be conducted. Second, field-based comparison of device prototypes with respect to different take-off surfaces (e.g. wet vs dry ground) will assess performance in realistic settings. The third set of experiments will deploy our jumping UAV in a test field at the Lincoln Institute for Agri-foot Technology (LIAT) Riseholme (farm) campus to evaluate the accuracy and reliability of the robot to locate and sense the same plant repeatedly, taking off from different surfaces. The ideal candidate will have an interest in precision agriculture, a knack for mechanical design and computer programming, and be intrigued by a creative approach to problem solving in an interdisciplinary environment.

Specific requirements include:

· At minimum, a 2.1 degree in a relevant or related discipline (for example, but not limited to: Physics, Mechanical Engineering, General Engineering, Electronic/Electrical Engineering, Computer Science, Data Science)

· Ability to demonstrate skills and/or experience relevant to the project subject area(s) of interest

· Experience programming in C/C++ or Python

· Evidence of ability to engage in scientific research and to work collaboratively as part of a team

· Excellent communication skills in written and spoken English.

PhD in Computer Vision/Machine Learning for Animal Welfare Monitoring

“Towards an open-source, equipment-agnostic framework for automated welfare monitoring in the home cage”

Funding for: UK and EU Students

Hours: Full-time

Start Date: Sepetember 2020

Duration: 36 months

The Laboratory of Vision Engineering (LoVE), in the School of Computer Science, is a team of internationally-recognised computer vision researchers in application areas spanning medical imaging, security, animal welfare, and the environment.

We are seeking to appoint a motivated, inquisitive PhD candidate to join a diverse team of researchers. The project will be to develop computer vision/machine learning techniques to monitor the behaviour of mice in the home cage, with a view towards developing a general-purpose open-source framework for uptake by the animal research community. This project is fully funded by The National Centre for the 3Rs (NC3Rs); an independent scientific organisation that seeks to minimise the use of animals and improve welfare standards where no viable alternative exists.

The successful student will develop anomaly detection methods to automatically identify welfare concerns from infrared video footage. Unsupervised deep learning techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs) offer great promise for the detection of such anomalies and may be further strengthened by so-called “self-supervised” learning approaches. The students will develop techniques along these lines using hundreds of hours of video footage acquired of mice via an infrared camera. This project will provide ample opportunity to explore a wide range of techniques for the detection and localisation of abnormal behavioural events using real-world data.

Funding Details:

Full UK/EU/tuition fees + £15,245.04 per annum stipend + other benefits

Eligibility Criteria:

This funding is only available to UK/EU candidates ordinarily residing in the UK for at least three years prior to the start of the studentship. This excludes those residing in the UK wholly or mainly for the purpose of full-time education. For more information, please refer to the UKRI guidance:

Academic Criteria:

The successful candidate will have a Master's (ideally) or Bachelor’s degree (2:1 or above, with honours) in computer science, engineering, bioinformatics, or a related discipline. They should also demonstrate one or more of the following in their application:

  •  A firm grounding in Python, MATLAB, or a similar high-level programming language
  • Familiarity with classical image/video processing and computer vision techniques
  • Experience in using one or more deep learning libraries (e.g., Keras, TensorFlow, Torch)
  • Contributions to open-source software projects related to imaging/vision/machine learning
  • 1 or more years of experience in machine learning or (bio)statistics, either in academia or industry.

How to Apply:

Please email a CV, cover letter, copy of final year/Master’s project thesis, a copy of your highest qualification certificate/transcript, and two reference letters to: quoting the following reference: 2AJ-20-201.

Applications will be accepted until the position is filled.

All interviews will take place via video conferencing. If social distancing guidelines should remain in place by the start date, remote working will be strongly encouraged and supported.

PhD Studentship in Optimization of Cryogenic Systems for Next Generation X Ray Optics for Diamond II

Reference Number: 2AL-20-001

Project Lead: Dr Jonathan Griffiths

Diamond is about to upgrade to Diamond II, where the X-ray power is going to increase by a factor of two and the focal size and shape of the beam is to become more focused and symmetrical. This will require a rethinking of how the optics are cooled in order to cope with the higher heat loads and also ensure the projected levels of brilliance predicted for the new light source are achieved.

The successful student will be tasked with meeting this challenge and optimising the usage of LN2 needed to achieve Diamond’s goal. There is also a level of ultra-precision to be considered (for reference, a monochromator optic is required to keep its radius of curvature to greater than 50 km). This project will focus on:

  • Efficiency and environment: How can LN2 usage be monitored and optimised to cool and maintain the optics at operational temperature.
  • Materials: Can we use materials that respond more to the cooling effect or optical materials that don’t need to be so cold.
  • Design: New methodologies for how technology achieves cryogenic temperatures.

Funding Details

The PhD is due to start on 1 October 2020. It is a 4 year studentship. Funding contributions are: Diamond Light Source: 50% University of Lincoln: 50%. Home/EU fees are covered by the studentship. The annual stipend starts at £16,998 and increases year on year to £17,684 in year 4.

Eligibility Criteria

Open to all students of any nationality without restrictions (UK/EU and International). For international students to study at University of Lincoln you must hold a valid visa which entitles you to study at the University.

Academic Criteria:

The successful candidate should have, or expect to obtain, an undergraduate degree at 2.1 or above (or equivalent) in engineering, physics, or related subject area.

English Language Requirements:

IELTS with a minimum overall band score of 6.0 with no part of the test scored below 5.5 (or equivalent)

How to Apply

To apply for this studentship please email your CV (maximum 2 pages), a personal statement outlining explaining how your qualifications and experience meet the requirements (500 words) and contact details of two referees to, quoting the following reference: 2AL-20-001.

Shortlisted applicants will be contacted directly to arrange for an interview.

The closing date for applications is Tuesday 1 September 2020.

Informal enquiries to be made to Dr Jonathan Griffiths:

PhD Studentship in Intelligent Structures for Monochromators and Mirror Systems for Diamond II Optics

Collaborating Institution: Diamond Light Source

Reference: 2AL-20-2

Location: University of lincolnLincoln (the studentship will also involve time spent at the synchrotron building at Diamond Light Source in Oxfordshire).


As the plans for Diamond II upgrade to the Diamond synchrotron facility come together, optics will be required to perform under ever increasing heat loads to even tighter tolerances with greater levels of sophistication.

At present, complex optics such as monochromators are assembled with only calibrated spring arrangements and torque wrenches. These calibrated, off-the-shelf springs and other mechanical calibration methodologies are often incorrect in the force they generate, resulting in assemblies which do not perform as predicted. This leads to a process that is unsatisfactory, as variability inherent to fasteners and calibrated springs limits the control of the applied tension, making it extremely challenging to obtain distortion free optical surfaces that must be controlled to picometer resolution.

These errors are compounded as further assembly is carried out without a full understanding for their effects on other components. This results in the assembly, setup and fine-tuning being time consuming and resource intensive. Instead, it is proposed that the assembly needs to be modelled and carefully measured. The effects of all the applied fastener loads in combination must be understood. This requires detailed feedback of the exact strain state of each component in the assembly; however, currently there is a lack of viable technologies that can be easily applied to acquire this information.

Applications are invited to complete a PhD programme to investigate how the above issues are to be addressed. The need to ensure high precision assembly of optical components for use in the Diamond II synchrotron facility is paramount. This body of work will investigate the design of smart features that enable accurate assembly and their monitoring during service. The candidate will spend time at Diamond understanding the issues, developing and designing solutions from first principle also using FEA, and examining the solutions manufactured before being involved in their testing. Three key areas are to be explored:

  • Smart structures for assembly utilising acoustic signal processing and condition monitoring
  • Smart structures for in-vacuum assembly utilising image processing
  • Passive resonant structures for silicon optic monitoring during service

This project aims to facilitate the development of world leading knowledge and understanding of how high precision optical components perform in service as well as ensuring confidence in their assembly.

In addition to empirical investigations there will be software-based modelling and hardware design being required to support development and understanding of various devices.

Funding Details

The PhD is due to start on 1 October 2020. It is a 4 year, fully-funded studentship. Funding contributions are: Diamond Light Source, 50% University of Lincoln, 50%. Home/EU fees are covered by the studentship. The annual stipend starts at £16,998 and increases year on year to £17,684 in year 4.

Eligibility Criteria

Open to all students of any nationality without restrictions (UK/EU and International). For international students to study at University of Lincoln you must hold a valid visa which entitles you to study at the University.

Academic Criteria:

The successful candidate should have, or expect to obtain, an undergraduate degree at 2.1 or above (or equivalent) in engineering, physics, or related subject area.

English Language Requirements:

IELTS with a minimum overall band score of 6.0 with no part of the test scored below 5.5 (or equivalent)

How to Apply

To apply for this studentship please email your CV (maximum 2 pages), a personal statement outlining explaining how your qualifications and experience meet the requirements (500 words) and contact details of two referees to, quoting the following reference: 2AL-20-2

Shortlisted applicants will be contacted directly to arrange for an interview.

The closing date for applications is Tuesday 1 September 2020.

Informal enquiries to be made to Dr Jonathan Griffiths:

PhD Studentship in UAV-Aided Smart Data Collection and Processing for Wireless Sensor Networks using Machine-Learning Techniques

Reference Number: ENGWSN001

Project Lead: Dr Edmond Nurellari

Applications are invited for a fully funded PhD studentship at the School of Engineering, in ‘UAV-Aided Smart Data Collection and Processing for Wireless Sensor Networks using Machine-Learning Techniques’. Students will be part of the Communication, Networks, and Embedded Systems (CNES) research group within School of Engineering. Most of the research activities within CNES are externally funded. This is a unique and exciting opportunity to further a career in Internet of Things and WSNs. The CNES team includes both academics and industrial experts, and team-working is an important part of this project.

The research will demonstrate how the Internet of Things and emerging digital technologies enable low cost continuous data collection and without the need for onsite instrument and data specialists. Specifically, this research study has the following objectives:

  • Design and implement practical WSN demonstrators and embed Machine Learning techniques to perform data analysis
  • Develop the sensing (measuring) module, communication module and statistical prediction model. Combine all the selected off the shelf sensors (e.g., the temperature, pH, wind sensors, etc.) and connect to the cloud.
  • Develop algorithms within the cloud to predict undesired Region of Interest (ROI) condition. Develop the user interface (pc or/and smartphone).
  • Evaluation of developed technologies and methodologies.

Funding Details:

Full UK/EU/ tuition fees + £15,000 per annum stipend + other benefits.

Eligibility Criteria:

Residency: This studentships is open to all students of any nationality without restrictions (UK/EU and International).

International students (non-EU) should note that this funding will cover tuition fees levels for UK/EU students only. To study at University of Lincoln, you must hold a valid visa which entitles you to study at the University.

Academic Criteria:          

Candidates should possess a honours degree (1st, 2.1 or, equivalent), and/or a Master's degree in the area of Electrical/Electronics, Computer Science, Engineering, Robotics, Embedded Systems, Mechatronics, or related fields. Candidates should hold an interest in topics including modelling and designing Wireless Sensor Networks, and some experience of programming in C/C++ or embedded platforms/related design tool, data processing and visualization, data mining, and intelligent systems.

English Language Requirements:

Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS).

Informal enquiries can be made by e-mail to Dr Edmond Nurellari:

How To Apply

Apply Now

The closing date for applications is 30 March 2020. However, applications will be accepted until the position is filled.

Interviews for shortlisted candidates are expected to take place during April 2020, with a start date in June 2020.


Recovery of Metals/raw Materials from Wastes, Residues, and Ashes Produced Through the Thermochemical Conversion of Phytomined Biomass

PhD Fee Waiver Scholarship

Reference Number: ENG008

Project Leads: Dr Jose Gonzalez-Rodriguez, Dr Abby Samson


Heavy metal contaminated land covers a large expanse throughout Europe and is often considered unsuitable for agriculture. Certain biomass crops have been identified as capable of phytoremediating such land (including Miscanthus).

This presents great opportunities in terms of land utilisation and remediation, but also poses interesting challenges with the production of a now contaminated biomass fuel. The heavy metal uptake of this fuel makes it unsuitable for traditional thermochemical use. There is also an excellent opportunity to recover metals and raw materials from this fuel, which would aid in the EU’s challenge of finding new sources of raw materials and also render this contaminated fuel usable once again.

This PhD project will focus on the following issues:

  • Metal uptake by biomass from contaminated land (efficacy of uptake, determination of which metals are absorbed, metal concentrations and partitioning within the plant).
  • Partitioning and fate of metals within waste streams from typical thermochemical conversion routes (pyrolysis, combustion and gasification). 
  • Development of novel methods for metal recovery from untreated fuels as well as from each of the waste streams through the use of molecularly imprinted polymers.
  • Development of online sensors to aid in the detection of the metals in the different treatment streams.

Funding Package

The scholarship covers tuition fees for the PhD up to the value of the UK/EU fee level. Overseas students may apply and the student will be responsible for the difference between the UK/EU, and overseas fee level. The grant holder will also be exempt from paying bench fees.

As living costs are not covered by this award, it is assumed that the grant holder will be applying for a PhD loan from the government (£25,000) for the three years research in order to guarantee a steady income to support themselves during their studies. However, candidates may secure other funds to pay for their living and maintenance during their PhD.


For informal enquiries, please contact Dr Jose Gonzalez-Rodriguez ( or Dr Abby Samson ( for further information and to discuss details of the application.

Entry Requirements

Applicants should have an appropriate Master’s degree. Suitably qualified candidates worldwide may apply, although International students must self-fund the difference between the International and UK/EU fee rate. 

Apply Now


Applications are open until the vacancy is filled.

Open for UK, EU, and Overseas Students.

Tuition Fees included (capped at UK/EU level).

Living expenses are not included.


College of Social Science

Improving Prehospital Care Using Ambulance Data

PhD Funded Studentship

Reference Number: HSC2019-4

Supervision team: Professor Niro Siriwardena, Professor Graham Law.


Applications are invited for a fully funded studentship associated with the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (NIHR-ARC East Midlands) East Midlands from outstanding, highly-motivated students to join a thriving research environment based at the Community and Health Research Unit at the University of Lincoln, in one of the world’s great small cities. Candidates are sought with interests in prehospital ambulance care and statistical analysis of ambulance data.

The successful candidate will join an active and growing research centre, the Community and Health Research Unit (CaHRU) at the University of Lincoln working on prehospital quality and outcomes research.

Experience in scientific research in relation to health will be desirable but not essential. Strong scientific qualities will be essential.

Contact: Professor Niro Siriwardena: or Professor Graham Law:

How to Apply

Applicants should have a first or upper second class honours degree or equivalent in a relevant area. Applicants with a relevant Masters are particularly welcome. Applicants should possess excellent report writing and English language communication skills and an ability to work to deadlines.

An application of a 2-page CV and 2-page covering letter including a personal statement demonstrating how your experience to date prepares you to undertake PhD level research, and summary of the research should be emailed to Maureen Young: Please quote the project ID in the subject line of the email.

Those called for interview will be required to prepare a brief presentation.

Closing date: 19 July 2020

Interview date: Friday 31 July 2020

Start date: September 2020

Eligibility and Funding

Suitably qualified candidates worldwide may apply, although international students must self-fund the difference between the International and the UK/EU fee rate.

£15,009 per annum stipend – stipend will be paid at the 2019/2020 UK Research & Innovation rate (

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Contact Us

If you would like to find out more about postgraduate study at the University of Lincoln or have any questions, please contact our Enquiries team.

Postgraduate Enquiries
University of Lincoln

Brayford Pool
+44 (0)1522 886644