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.
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.
Directed Assembly of Organometallic Complexes as Novel Electronic Constructs
Academic Contact – Dr Louis Adriaenssens (email@example.com), 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.
AI-based Multi-objective Decision Making for Efficient Energy Management of Smart Grids
Academic Contact: Dr Shouyong Jiang (firstname.lastname@example.org), 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.
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 (email@example.com), 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 (firstname.lastname@example.org), 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:
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:
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 (email@example.com), 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.
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 (firstname.lastname@example.org), 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:
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 (email@example.com), Senior Lecturer, School of Life Sciences
Prof Gregory Sutton (firstname.lastname@example.org), Royal Society University Research Fellow, School of Life Sciences
Prof Elizabeth Sklar (email@example.com), 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