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 in Agri-Food Robotics: AgriFoRwArdS

The University of Lincoln has launched 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 is creating 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 for entry into the CDT programme, starting in October 2021, will open in October 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

PhD Studentships: AHRC GCRF Studentships for Mobile Arts for Peace

University of Lincoln is offering two fully-funded PhD studentships in connection to a four-year Arts and Humanities Research Council (AHRC) Global Challenges Research Fund (GCRF) project entitled Mobile Arts for Peace (MAP): Informing the National Curriculum and Youth Policy for Peacebuilding in Kyrgyzstan, Rwanda, Indonesia, and Nepal. 

Project Website: http://map.blogs.lincoln.ac.uk

MAP aims to provide a comparative approach on the use of interdisciplinary arts-based research methods for peacebuilding in Kyrgyzstan, Rwanda, Indonesia, and Nepal. MAP has the overall goals of first, influencing curricula and approaches to working with in- and out-of-school youth to address global challenges and second, creating structures and modes of communication between youth and policymakers from the local to global.

MAP will research with youth, educators, cultural artists, civil society workers, and policymakers to address the gap between needs assessment, curricula development, policy intent, and youth priorities. It will aim to scale-up arts-based peacebuilding initiatives and leverage greater policy influence, while also accelerating the scope and depth of this network using a comparative learning and mentoring approach for South-South knowledge exchange across linguistic divides. The position entails the engagement with 8 national and international partners and 25 sub-projects working with academics, artists, civil society workers, policymakers, and young people from around the globe.

The PhD studentships will ideally be focused on:

1) impact, monitoring and evaluation; and

2) adaptation of cultural forms and performance for peacebuilding approaches.

Projects can explore, but are not limited to, topics such as youth engagement, system change and societal transformations, education for peacebuilding, and applied performance working in post-conflict contexts.

The successful applicant will be supported by a supervisory team with considerable expertise and leadership in creative and participatory approaches, education, youth policy, and performance. The Principal Investigator of MAP, Professor Ananda Breed, will serve as the Director of Studies. They will also benefit from being part of a vibrant research culture in the College of Arts that is supported by the Centre for Creativity and Culture, the Lincoln Institute for Advanced Studies, and an international research team including co-investigators from partnering organisations including University College London, Open University, University of Rwanda, Tribhuvan University, Atma Jaya Catholic University, Foundation Tolerance International, Institute of Research and Dialogue for Peace and Human Rights Film Centre.

Contact: ABreed@lincoln.ac.uk and CBrennan@lincoln.ac.uk

Entry Requirements

Applicants should have a first or upper second class honours degree or equivalent in a relevant area. Applicants with a relevant Master's are particularly welcome. Experience in the following areas will be desirable, but not essential: youth advocacy, theatre for development, impact, monitoring, and evaluation.

Applicants should possess excellent report writing and English language communication skills and an ability to work to deadlines.

How to Apply

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 proposed research project should be e-mailed to Ananda Breed (ABreed@lincoln.ac.uk) and Christina Brennan (CBrennan@lincoln.ac.uk). Those called for interview will be required to prepare a presentation.

Please quote project ID in the subject line of the email.

Closing Date: 16 October 2020

Interviews: November 2020

Start Date: 2020/2021 academic year

Funding

This studentship is for a start date in the Academic Year of 2020/21 and covers the full PhD fees for a maximum of 3 years full-time study. The candidate will have a stipend/living allowance of £15,300 per annum. Tuition fees are included (for Home/EU/International fee level). The funding is open to UK, EU, and Overseas Students

Duration: 36 months

Reference: 2CH-20-1

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.

Eligibility

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 (ladriaenssens@lincoln.ac.uk), 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 (sjiang@lincoln.ac.uk), 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 (lzhang@lincoln.ac.uk), 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 (hmaior@lincoln.ac.uk), 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 (https://www.artinis.com/)
  • 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 (jamesbrown@lincoln.ac.uk), 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 (FPaillusson@lincoln.ac.uk), 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 (kelgeneidy@lincoln.ac.uk), 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 (efrasnelli@lincoln.ac.uk), Senior Lecturer, School of Life Sciences

Prof Gregory Sutton (gsutton@lincoln.ac.uk), Royal Society University Research Fellow, School of Life Sciences

Prof Elizabeth Sklar (esklar@lincoln.ac.uk), 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 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 jgriffiths@lincoln.ac.uk, 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: jgriffiths@lincoln.ac.uk

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).

Background

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 jgriffiths@lincoln.ac.uk, 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: jgriffiths@lincoln.ac.uk

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: enurellari@lincoln.ac.uk

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

Overview

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.

Contact: 

For informal enquiries, please contact Dr Jose Gonzalez-Rodriguez (jgonzalezrodriguez@lincoln.ac.uk) or Dr Abby Samson (asamson@lincoln.ac.uk) 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

Details

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.

Overview

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: nsiriwardena@lincoln.ac.uk or Professor Graham Law: glaw@lincoln.ac.uk

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: studentshipscss@lincoln.ac.uk. 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 (https://www.ukri.org/skills/funding-for-research-training/).

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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
Lincoln
LN6 7TS

pgenquiries@lincoln.ac.uk
+44 (0)1522 886644