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 are now open for entry into the CDT programme, starting in October 2021.

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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 Science

EPSRC logo

EPSRC Doctoral Training Partnership (DTP) Studentships

The University of Lincoln has received funding from the Engineering and Physical Sciences Research Council (EPSRC) to establish a Doctoral Training Partnership (DTP), which will provide skills and training to foster the next generation of world-class research leaders 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 2021-22 studentship application round and these are detailed below.

Studentship applications are now open for entry into the DTP programme, starting in October 2021.

Please make sure to check the eligibility criteria before you apply. Normally, 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. UK students will be eligible for a full studentship, covering the costs of Home fees, a stipend to support living costs for 3.5 years, and a generous research training support grant enabling international travel and participation in the leading conferences and symposia.

Although most DTP students must be UK residents, we also have an opportunity for an international (EU and non-EU) student. The international studentship award will be subject to eligibility, and also the availability of complementary funding (to provide the differential to the international fee rate). You should get in touch with the lead supervisor before applying this award.

Closing date: Midnight, 14 March 2021.

DTP PhD Application Form 2021-22


Smart Energy

Ultra-low loading Pt electrocatalysts based on carbon supported alloys for oxygen reduction in hydrogen fuel cells and their lifetime prediction

Academic contact: Dr Guanjie He (ghe@lincoln.ac.uk), Senior Lecturer, School of Chemistry

This project aims to develop sustainable and low-cost oxygen electrocatalyst based on the carbon framework supported ultra-low loading Pt alloy clusters to realise the high performance and stable energy outputs.

With global population and demands for energy increasing, non-renewable resources such as coal, oil, and natural gas are being rapidly depleted. Global challenges, such as air pollution and climate change, require urgent technological solutions. The UK government set the 25 Year Environment Plan in 2018, emphasising the increase of resource efficiency and the reduction of pollution and waste. The hydrogen fuel cell is one of the promising candidates for next-generation clean energy suppliers, but expensive electrocatalysts (relatively high loading Pt) on cathodes limited their further application.

This project aims at developing low-cost and high-performance oxygen reduction electrocatalyst based on the ultra-low loading Pt alloys on carbon framework. The mechanism will be studied via the advanced in-situ characterisation techniques, and the lifetime of the electrocatalysts and fuel cell devices will be monitored and estimated via the developed programme. The successful candidate will work with a skilled and interdisciplinary supervisorial team (materials chemistry, computer science, chemical engineering) from University of Lincoln, UCL, and Bramble Energy Ltd. The student will be trained for chemical synthesis, materials characterisation, electrochemical evaluation, computational coding and fuel cell fabrication.

Skills the candidate can learn:

- Materials synthesis, processing, and characterisation methods
- Electrochemical performance evaluation for oxygen reduction reactions
- Fabrication of fuel cell devices
- Computational coding for lifetime prediction of fuel cells
- Collaborative skills among academic field and industry

Ideal candidate:

- Should have, at a minimum, a 2.1 degree in chemistry, materials science, chemical engineering, or a related discipline
- Can demonstrate skills and experience (or an aptitude for mastering) the synthetic chemistry, energy device fabrication, and computational coding
- 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

Supervisory Team:

Dr Guanjie He, University of Lincoln
Dr Shouyong Jiang, University of Lincoln
Prof Dan Brett, University College London

Artificial intelligence for energy management optimisation in smart grid

Academic contact: Dr Shouyong Jiang (sjiang@lincoln.ac.uk), Senior Lecturer, School of Computer Science

Have you considered to optimise your household energy usage to reduce your bills and save the planet? Are you looking for approaches to schedule your energy consumption in response to energy pricing schemes imposed by energy providers? Smart grid technology represents an unprecedented opportunity to move the energy industry into a new era of reliability, availability, sustainability, and efficiency that will contribute to our economic and environmental health. This project aims to develop artificial intelligence approaches to efficient energy management that achieve demand-supply balance for stable, cost-effective operation of the smart grid system, maximising different stakeholders’ interests. In particular, the project will investigate AI approaches, including machine learning and/or bio-inspired optimisation, to optimise energy management from energy supply to energy consumption. It will look for novel solutions to trade-off the profit of energy suppliers and the bills of energy users while maintaining supply-demand balance of energy and lowering carbon emission.

The overall aim of the project is to develop a AI-based decision-making tool to improve energy management of smart grid that balances different stakeholders’ interests including energy suppliers’ profit, end users’ energy consumption and bill, and governments’ net zero carbon goal, while ensuring demand-supply balance. In order to reach this aim, the project has the following key objectives:

- Investigate computational models of electricity markets of smart grid systems at different scales, including smart home, microgrid, or distributed resource systems.

- Develop machine learning algorithms for demand response, including dynamic pricing, from energy suppliers’ perspectives.

- Develop machine learning algorithms to support smart scheduling of power consumption at peak time from energy users perspectives.

- Multi-criteria optimisation of energy management from both energy suppliers’ and end users’ perspectives.
Test and evaluate the proposed approaches in real-scenario experimental platforms.

You will be working with the Machine Learning group, within the School of Engineering and School of Mathematics and Physics at the University of Lincoln. This is an exciting opportunity for developing a career in AI for smart energy and deliver world-leading research that impacts our economy and society.

Skills the candidate can learn:

Discipline specific knowledge, including machine learning, data analysis, modelling and optimisation, and smart energy technology. The candidate can develop the ability to gather and interpret information, and ability to analyse data. They can also develop problem-solving skills, project management skills, and oral and written communication skills, alongside the ability to work independently and as a team member. The candidate will also receive a broad set of training on scientific research and transferable skills.
 
Ideal candidate:

Interested applicants should carry, 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 have the ability to engage in scientific research and to work collaboratively as part of a team.

They must have a research interest and experience in at least one of the following areas: data analysis, artificial intelligence, machine learning, mathematical modelling, game theory, energy management, operational research, and optimisation approaches (evolutionary computation, and multi-criteria decision making, etc).

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.

Supervisory Team:

Dr Shouyong Jiang, University of Lincoln
Dr Matthew Watkins, University of Lincoln
Prof Chris Bingham, University of Lincoln
Dr Sepehr Maleki, University of Lincoln


Medical Diagnosis and Healthcare Support Systems

AI enabled biomarker identification from exhaled breath condensates for early detection of secondary infection in chronic obstructive pulmonary disease (COPD) patients

Academic contact: Dr Wenting Duan (wduan@lincoln.ac.uk), Senior Lecturer, School of Computer Science

This project aims to develop software approaches to identify biomarkers for secondary infections in COPD using non-invasive exhaled breath samples. Using this approach, we aim to identify specific biomarkers that can be used to predict/diagnose the onset of a secondary infection prior to the patient becoming symptomatic as part of routine clinical monitoring. With this approach we hope to enable proactive clinical management of COPD patients through the early detection of secondary infections to avoid exacerbations of COPD which can lead to hospitalisation.

Chronic obstructive pulmonary disease (COPD) is characterised by long-term breathing problems which can be exacerbated by secondary infections. Current diagnosis of secondary infections in COPD patients requires invasive sampling which makes routine screening labour intensive, unpleasant and impractical for patients. Two schools at the University of Lincoln, the School of Computer Science and the School of Life Sciences are collaborating to develop software approaches to identify biomarkers for secondary infections in COPD using non-invasive exhaled breath samples.

The main aim of the PhD project is to develop algorithms for analysis of mass spectrometry (MS) data that can detect infection in COPD from non-invasive breath samples. You will analyse MS data exploiting advanced pattern recognition, mass spectrometry imaging, and machine/deep learning techniques for infection classification and prediction.

The successful student will be associated with the Laboratory of Vision Engineering in School of Computer Science, but will also be part of a cross-discipline collaboration amongst several research groups including the Laboratory of Vision Engineering, the Machine Learning group, the Diabetes, Metabolism and Inflammation research group, and Animal Behaviour and Welfare group. By joining the Doctoral Training Partnership programme at the University of Lincoln, you will be also conducting research together with other PhD and post-doctoral students in a supportive and intellectually stimulating environment.

Skills the candidate can learn:

Whilst pursing this project, the student can gain extensive knowledge of the state-of-art of image processing, machine learning, and deep learning techniques. They will have the chance to develop advanced Matlab, Python or R programming skills, and obtain experience of cross-disciplinary working with academics, clinicians, and other researchers. The student will also receive broad training on research and transferable skills including scientific writing, presentation skills, and project management.

Ideal candidate should have:

- A first class, upper second class (2:1) or Master's qualification in Computer Science, Bioinformatics, or equivalent subject area

- Good knowledge of image processing or machine/deep learning

- Good programming skills of Matlab, Python, or R programming

- Capability to work independently and as part of a team

-  A good mathematical background

- Excellent written and oral communication skills in English

- A real passion and commitment for research


Supervisory Team:

Dr Wenting Duan, University of Lincoln
Dr Shouyon Jinag, University of Lincoln
Dr Neil Holden, University of Lincoln

Localising breast cancer risk in mammograms

Academic contact: Dr Faraz Janan (fjanan@lincoln.ac.uk), Senior Lecturer, School of Computer Science

The project aims to develop a breast cancer diagnosis support system by localising the future risk of developing cancer, in particular associated with mammographic focal densities. By analysing time series data with new mammographic image analysis framework, the project aims to propose a CAD system that can flag women likely to develop breast cancer in near future. The system should also be able to suggest among the bilateral breast, as well as the location/quadrant where the risk of developing cancer is higher.  

Applications are invited for a PhD studentship to work on a project using a range of advanced AI and image analysis techniques to study mammographic images in the context of breast cancer risk. The project will require a full-time research commitment and will be based in the Laboratory of Vision Engineering at Lincoln. It will combine mammographic density quantification with an AI based classification and pattern recognition framework (desirably Deep Learning methods). It would evaluate the methods developed on mammograms acquired from the Optimam that have negative priors, CC and MLO views available for both breasts and depict biopsy-proven cancers, as well as normal cases. This would help us to assess the effectiveness of a CAD system and its suitability in a clinical set up.

The 3-year project will be carried out in close collaboration with scientists and breast radiologists at Oxford and Lincoln. The successful candidate will be required to apply, develop and program algorithms in the area of computer vision and machine learning, while applied to mammographic images -including but not limited to x-ray and Digital Breast Tomosynthesis (DBT). Interested candidates should send their CVs (including references) to fjanan@lincoln.ac.uk.

Skills the candidate can learn:

Mammographic image analysis, clinical aspects of breast cancer detection and diagnostics, deep learning methods applied to medical imaging

Ideal candidate:

A strong academic track record with a 2:1 or higher degree in computer science, mathematics, biomedical engineering, electrical(computer) engineering’s or its equivalent if outside the UK.  The desirable candidate should have an excellent performance in a relevant postgraduate degree. The candidate is expected to demonstrate expertise of coding in Matlab and python, with good knowledge of image processing techniques. A prior working experience of deep learning methods is desirable. The candidate should be willing to work in close collaboration with clinical radiologists.

Supervisory Team:

Dr Faraz Janan, University of Lincoln
Dr Louise Wilkinson, Oxford University Hospitals NHS Trust
Prof Xujiong Ye, University of Lincoln
Dr Tryphon Lambrou, University of Lincoln

AI-based multimodal sensor information fusion for fall detection and prediction

Academic contact: Dr Miao Yu (myu@lincoln.ac.uk) Senior Lecturer, School of Computer Science

Novel AI techniques for fusing information from multiple sensor modalities, to timely detect/predict falls will be developed in this project, with the aim of minimising the adverse effects of falling on vulnerable people.  Related techniques which will be developed in this project will contribute to falls prevention and the reduction of associated healthcare costs of falls, such as fractured neck of femur and prolonged hospitalisation. Moreover, it is anticipated that the techniques developed in this project will contribute to the development of novel healthcare support systems for detecting/predicting/preventing falls, as well as optimise independent living for older adults and other related stakeholders by reducing their risks of falling in their independent living.

Falls and fractures are a common and a serious health issue faced by older people in England and globally. Around a third of people aged 65 and over, and around half of people aged 80 and over, fall at least once a year. Falling is a cause of distress, pain, injury, loss of confidence, loss of independence, and mortality. Detecting/predicting the occurrence of falls and reducing falls and associated fractures are important for maintaining the health, wellbeing, and independence of older people and other related stakeholders in the health and social care sectors.

Considering the importance of detecting/predicting falls and for minimising the adverse effects of falling on vulnerable people, this project aims to detect and predict the risk/likelihood of falls based on multimodal sensor modalities (vision sensor and wearable sensor). The candidate is expected to develop novel signal processing, machine learning and deep learning algorithms, to optimally extract and fuse information from vision and wearable sensors’ recordings of a person’s movements, for detecting possible falls as well as predicting the fall risks associated with some normal daily activities (such as walking or sitting). Novel automatic fall detection/prediction techniques based on multimodal sensors are expected to be developed at the end of the project.

The successful candidate will be given the opportunity to work across disciplines and engage with both experts across different Schools in the University of Lincoln and experienced physiotherapists in the NHS and private sector. Moreover, the successful candidate may have internship opportunities in prestigious AI healthcare start-ups specialised in wearable and vision sensors for healthcare.

Skills and experience you can gain are expected to include:

- Opportunities to develop expertise in machine learning and deep learning and their applications in healthcare
- Quantitative research skills and applied statistics
- Critical thinking and problem-solving abilities
- Applied data science/machine learning/deep learning
- Strong verbal and written communication skills
- Opportunities to present research outputs at national and international venues.

Internship opportunities exist at Vitrue Health and Activinsights

Ideal candidate:

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.

Supervisory Team:

Dr Miao Yu, University of Lincoln
Prof Simon Parsons, University of Lincoln
Ross Clifford, University of Lincoln


Bio-physics Inspired Robotics

A Miniaturised Stiffness-controllable Soft Medical Manipulator

Academic contact: Dr Khaled Elgeneidy (kelgeneidy@lincoln.ac.uk), Senior Lecturer, School of Engineering

This PhD studentship is focused on developing a miniaturised soft medical manipulator that utilises a novel controlled-stiffening mechanism to enhance tool stability and force output. This research will involve studying particle jamming through bio-physical modelling and translating that information into an engineering model. Designing this bio-inspired medical robot that can actively change its stiffness, in response to sensor data, and operate in a controlled way is the main focus of this project.

Throughout this project, the PhD candidate will work closely with clinical partners to guide the development of the soft manipulator. The internal supervisory team from the University of Lincoln combines expertise in soft robotics (Dr Khaled Elgeneidy), medical robotics (Prof Mini C. Saaj), and computational physics (Dr Fabien Paillusson). The candidate will carry out research in the Bio-robotics and Medical Technologies Laboratory at the University of Lincoln. There will be opportunities to work collaboratively with Lincoln Medical School. Additionally, the project will be supported by external clinical co-supervisor, Dr Mohamed Thaha, from St Bartholomew’s Hospital, Queen Mary University of London as well as Dr Fulvio Forni from the University of Cambridge, who will act as an external academic co-supervisor.

Supervisory Team:

Dr Khaled Elgeneidy, University of Lincoln
Prof Mini C. Saaj, University of Lincoln
Dr Fabien Paillusson, University of Lincoln

AI enabled haptic-guided teleoperation: a study on Human-Robot Interaction

Academic contact: Dr Amir Ghalamzan Esfahani (aghalamzanesfahani@lincoln.ac.uk), Associate Professor, Lincoln Institute for Agri-Food Technology

This project aims at developing and testing novel teleoperating robot manipulators and assess their effectiveness. You will be specialising in Human Robot Interaction and will study how various robotic features (such as intelligent haptic guidance) impacts performance, operators’ mental workload, subjective experience, and physiological and brain data.

We are offering a fantastic multi-disciplinary PhD opportunity, fully funded, working with the Lincoln Centre for Autonomous Systems (LCAS), the interactive technologies lab (intlab), and the School of Psychology.
While we have extensively studied haptic-guided shared control for telemanipulation for remote handling in extreme environments, this PhD student will have the opportunity to study this in a wider context and application domain, such as agri-food robotics, and medical robotics. The student will have the opportunity to work with state-of-the-art robotic systems/equipment (e.g. Franka-Emika manipulators and Hpatin Virtuose™ 6D) and benefit from the setups at our group developed during several EU-H2020 and UK-EPSRC funded collaborative projects.

This interdisciplinary project is related to the design, development and implementation of AI, machine learning, and/or robot control solutions for haptic-guided shared control to facilitate teleoperating a robotic manipulator for human users. This project also involves evaluation techniques to assess operators’ mental workload and effort based on physiological and brain data by using portable Brain-Computer Interfaces and data science techniques to reveal the effectiveness of each developed technology. Full training will be provided.

By joining the Doctoral Training Partnership programme at the University of Lincoln you will participate in the fully funded 3.5 years programme supported by a multidisciplinary supervisory team across 3 different schools in the College of Science (School of Computer Science and the intLab, School of Psychology, and Lincoln Institute of Agri-food Technology).

Training opportunities:

- The PhD student will work with the state-of-the-art robotic equipment.
-  The can gain deep knowledge in Robotic Manipulation, Teleoperation, Control, ML and data analysis and apply them in the real experimentations
- They can gain significant knowledge for Human Robot Interaction studies and Data Analysis
- Develop analytical, problem-solving and research skills
-  Develop coding skills (C++, Python and ROS)
- Work in collaboration with an active team of researchers within L-CAS and across collaborating universities, including the University of Cambridge, Birmingham, Bristol and QMUL.

They will be working in a collaborative and stimulating environment, strengthened by the cohort-driven activities, where knowledge sharing and joint problem solving is the norm. The multidisciplinary nature of the programme will make you think about problems from a whole new perspective and explore innovative ideas.

We are seeking talented candidates with:

- MSc (first or upper second-class) in Robotics, AI, Human Factors, Human Computer Interaction, Computer Science, Mechanical Engineering, Electronics, Cybernetics, or related scientific discipline. Applicants who graduated in the UK with a strong first degree, or complementary industry experience are also considered.

- Good knowledge of robotics, control, ML, AI, Human Robot Interaction.

- Programming skills (Proficient C++ and/or Python)

- ROS experience is considered a strong plus

- Oral and written fluency in English

- Good communication, team working and presentation skills

- Experience with real robotic systems is a strong plus.

Supervisory Team:

Dr Amir Ghalamzan Esfahani, University of Lincoln
Dr Horia Maior, University of Lincoln
Dr Julia Foecker, University of Lincoln

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

This project aims to develop physical models of collective behaviour derived from biological systems comprising self-propelling agents, such as schools of fish and flocks of birds, and to then transform them into swarming behaviours of mobile robots. The ultimate aim is to improve the design of complex behaviours for robot swarms by integrating elements of collective intelligence inspired by biological agents.
Active Matter is an emerging interdisciplinary field in physics and applied mathematics that refers to systems comprising interacting agents that can drive their own motion (such as birds, fish, insects, “smart” artificial micro-particles, or bio-mimicking robots). Active Matter systems are 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 agricultural applications making use of their self-assembling properties and resilience to external influences. In Active Matter systems, simple sets of rules can lead to many rich phases of collective behaviours. There is ample opportunity to develop new classes of rules that can give rise to never-before-seen phases, and ultimately provide insights into how to reverse-engineer rules for targeted goals.

Active Matter systems are of particular interest with respect to swarm robotic systems where the behaviour of individual robots is affected by the fluid dynamics of their environment; for example, aerial drones whose position may drift when buffeted by gusts of wind, or surface-water/underwater vessels that may be dragged along by water currents. These swarm robotic systems must coordinate their movement despite the influence of the fluid environment they inhabit. By translating the mechanics of Active Matter systems to swarm robotic systems, we hope to improve the performance of embodied agents used in real-world applications. This novel approach would be particularly beneficial in GPS-denied environments such as deep-sea exploration, where individual robots must remain aggregated without the aid of an external frame of reference.

This interdisciplinary project at the interface of physics, computational modelling, and robotics will develop new theoretical and computational models for such systems and validate them on physical robotic swarms. It will broadly consist of three main tasks:

- Formulating a set of microscopic rules deduced from a set of bio-inspired Active Matter systems, and formulating a statistical physics description of a large groups of such Active Matter systems

- Deriving continuous field models starting from the microscopic rules defined in task (1) to describe the collective behaviour of Active Matter

- Validating the developed models through their implementation on aerial/surface-water robot swarms – initially in the widely-used ARGoS multi-robot simulator, and then on physical hardware

This project combines complementary expertise from two schools: the School of Mathematics and Physics and the School of Computer Science, building on existing research. The successful student will be associated with the Centre for Computational Physics and the Lincoln Centre for Autonomous Systems (L-CAS), of which the supervisory team are members.

Skills the candidate can learn:

- Theoretical and computational modelling
- Design of control algorithms for robot swarms
- Interdisciplinary collaboration skills
- National and international collaboration skills
- Technical oral presentation and written communication skills

Ideal candidates:

Interested applicants should hold, at a minimum, a 2.1 degree in either Physics, Mathematics, Computer Science, Engineering, or a related discipline. Applicants with a relevant Master's degree are particularly welcome. The candidate is expected to have good communication and teamwork skills and must be motivated to learn new things. Interested applicants are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest.

Supervisory Team:

Dr Fabien Paillusson, University of Lincoln
Dr Alan Millard, University of Lincoln
Prof Andrei Zvelindovsky, University of Lincoln


As an institutional Athena SWAN Bronze Award holder, we are committed to advancing gender equality in STEM, therefore female applicants are strongly encouraged to apply.

 

Collaborative Training Partnership for Fruit Crop Research (CTP FCR) funded by BBSRC and Industry

High-throughput Robotic Phenotyping of Fruit Traits for Automatic Strawberry Harvesting

REFERENCE: CTP_FCR_2021_11

Supervisors: Dr Grzegorz Cielniak (University of Lincoln); Mr Adam Whitehouse, Dr Helen Cockerton (NIAB EMR)

The successful student will be registered with the University of Lincoln and mainly based at the University's campus. Starting in October 2021, the successful candidate should have (or expect to have) an Honours Degree (or equivalent) with a minimum of 2.1 in Computer Science/Computer Engineering graduates, and other disciplines with strong mathematical and programming skills. Prior experience in deploying technology in agriculture or horticulture is a plus.

Background

Fruit-picking robots are a promising technological solution to the labour problems faced by the soft fruit industry. The main obstacle for further development of successful robotic picking systems is a lack of general understanding which fruit variety is the most suitable for the task.

Objectives and Approaches

This PhD project proposes to develop new automated phenotyping techniques deployed infield on a mobile robot providing high-throughput, high-fidelity indicators of strawberry varieties indicating their suitability for robotic harvest. The study will investigate techniques based on plant/fruit geometry (i.e. 3D) providing traits about the phenology of the variety and external fruit and plant characteristics. The approach will overcome the limitations of the laboratory-based phenotyping systems by exploiting an autonomous mobile robot to enable rapid identification of multiple traits in the field. The fundamental knowledge and practical solutions to robotic phenotyping will benefit the soft fruit industry, robotics companies, and academia, driving future berry breeding programs and novel robot designs.

Training

The successful candidate will have access to state-of-the-art research farms equipped with industrial fruit production facilities, as well as agricultural robots with advanced sensors and tools, including the world-leading Thorvald platform.

How to Apply

Anyone interested should fill the online application form before the deadline of 8 February 2021.

If you need further help or clarification, please contact recruitmentctp@emr.ac.uk

 

 

PhD GTA Funded Studentship

Experimental and computational studies of the methanol synthesis catalyst – where is the hydrogen?

The PhD is jointly funded by the University of Lincoln and the ISIS Neutron and Muon Facility in Oxfordshire.

The synthesis of methanol from CO, CO2 and H2 is an enormous business - 75 million tonnes in 2015. The process uses a Cu/ZnO/Al2O3 (65:25:10) catalyst that was originally developed by ICI in the 1960s and operates in the range 200–300 °C and 10–100 bar. In view of its industrial importance, the catalyst has been extensively studied. There is general agreement that at low temperature the reaction proceeds by hydrogenation of CO2 to formate and then stepwise addition of hydrogen to methanol. At high temperature, CO hydrogenation also becomes important. While the roles of CO2 and CO have been extensively investigated and are well-characterised, the hydrogen component has been much less studied. It is generally believed that hydrogen dissociates on the copper, but adsorbed hydrogen has not been detected. H2 dissociates on ZnO to give hydroxyls and Zn-H species, but only the former have been observed on working catalysts. The aim of this project is a combined experimental and computational study to characterise the hydrogen present on a commercial, working methanol synthesis catalyst.

This project will have a computational aspect to be carried out at the University of Lincoln and an experimental aspect to be carried out at the ISIS Neutron and Muon Facility (Harwell Campus, Oxfordshire). The computational aspect will be to use density functional theory-based quantum chemical simulations to investigate the state of hydrogen on the catalyst. Initial work to provide training in the methodology will be to study the adsorption of hydrogen on the low index faces of copper and on ZnO and the Zn-doped Cu surfaces. Subsequent work will investigate extended systems that include at least two of the three catalyst components on which the detailed reaction mechanism of the methanol conversion from CO2 will be investigated. A range of computational methods will used including lattice dynamics, ab initio molecular dynamics and time-dependent density functional theory.

The experimental work will use a commercial Cu/ZnO/Al2O3 catalyst. Neutron scattering methods will be employed to investigate how the adsorbates and the catalyst change with different reaction conditions and time on stream. The emphasis will be to find and study adsorbed hydrogen, so where appropriate, hydrogen on model systems such as Raney Cu or pure ZnO will also be studied. As part of this work, we will improve our ability to produce samples at a particular point along a reaction coordinate by the implementation of UV-vis spectroscopy on an existing preparation rig designed to produce the large (10-50 g) samples required for neutron scattering studies of catalysts. At a later stage in the project, we will also implement Raman spectroscopy on the same rig. We will also modify an existing system for simultaneous Raman/neutron scattering measurements to enable gas handling experiments. The upgrades to the catalyst preparation rig will be of value to other groups that also use ISIS and some collaboration with these will form part of the project.

Informal enquiries are welcomed and should be directed to either Dr Arunabhiram Chutia (achutia@lincoln.ac.uk) or Prof Stewart Parker (stewart.parker@stfc.ac.uk).

Entry Requirements

Applicants should hold, or expect to receive, an MSc in chemistry or an honours degree in chemistry (first or upper second class honours degree), or the equivalent.

The project will require an extended stay (12-18 months) at the Harwell campus in Oxfordshire.

How to Apply

Formal applications should be made via the University of Lincoln’s online application form.

Closing Date: Saturday 31 July 2021 or until filled.

Interviews: TBC

Start Date: Monday 4 October 2021

Funding

This studentship is for a start date in the Academic Year of 2021/22 and covers the full PhD fees for a maximum of 3.5 years full-time study. The candidate will have a stipend/living allowance of £15,609 per annum. Tuition fees are included (for UK fee level).

The PhD is jointly funded by the University of Lincoln and the ISIS Neutron and Muon Facility. It includes UK fees and a stipend. Travel and subsistence for meetings and conferences up to £2k per annum is also available.

Duration: 42 months

Reference: 2CA-21-1

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. 

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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

PhD GTA Funded Studentship: Education and Training in Lincolnshire Police

This project funded by Lincolnshire Police offers an opportunity to spend three years empirically assessing the policing education qualification framework (PEQF). This assessment will involve following a cohort of police trainees in Lincolnshire Police and conducting focus groups, surveys and interviews. You will also be comparing your results with research, to be conducted by you, in another comparable police force.

The successful candidate would ideally have a practical awareness of criminal justice, although expertise in policing is not required. Previous experience of conducting empirical research is desirable, but not essential.

For further details contact Professor Karen Harrison: karenharrison@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 Masters degree are particularly welcome.

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 1-page CV and 1-page covering letter including a brief insight into the research topic area should be e-mailed to Maureen Young (studentshipscss@lincoln.ac.uk). Those called for interview will be required to complete an application form and prepare a presentation. Please quote the above reference number in the subject line of the email.

Closing Date: Friday 30 October

Interviews: Friday 13 November 2020

Start date: Tuesday 1 December 2020

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 three years full-time study. The candidate will have a stipend/living allowance of £15,258 per annum. Tuition fees are included (for Home/EU fee level).

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

The studentship may require you to do up to six hours of teaching or related work per week, the income from which will go towards the cost of your tuition fees and any surplus fees will be paid by the College

Duration: 36 months

Reference: 2AS-20-1

 

Improving Prehospital Care Using Ambulance Data

ARC 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 and/or qualitative analysis.

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: 28 February 2021

Interview date: Week commencing 8 March 2021

Start date: May-November 2021

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