Research Studentships

PhD Studentship

AI-based Novel Biomarkers Discovery for Diagnosing and Grading the Severity of Spinal Disorders with MRIs

Supervisory Team: Dr Lei Zhang, Prof Xujiong Ye, Prof Paul Lee

Background

We are offering a full 3-year PhD studentship jointly funded by the University of Lincoln and MSK Doctors Ltd. The successful candidate will join the Laboratory of Vision Engineering in the School of Computer Science, in close collaboration with MSK Doctors Ltd (https://www.mskdoctors.com/), which is an independent private medical clinic specialising in the management of musculoskeletal (MSK) conditions.

Project Description

Magnetic resonance imaging (MRI) is widely used to assess spinal disorders (e.g. lumbar spine deformities, intervertebral disc degeneration, osteoporosis, vertebral fractures etc.), as it provides non-invasive soft tissue visualisation with enhanced contrast alongside different modalities.

However, traditional manual image interpretation(reading) in clinical practice is prolonged and there can be variability in manual diagnoses, subject to the radiologist's experience. Automated AI based solutions can help to reduce the diagnostics errors caused by human clinical practice, and increase the efficiency of the clinical workflow. This research aims therefore to develop novel explainable AI approaches for diagnosing and grading the severity of spinal disorders with MRIs.

Research Aims

This research is to identify novel biomarkers from MRIs using state-of-the-art AI models to support diagnosis of spinal disorders, of which the efficiency, accuracy, and robustness will be validated in the clinical practice. The specific objectives are:

• To conduct requirement analysis and engage stakeholders in the co-design of the AI solution used for clinical practice
• To develop novel explainable AI approaches for automated lumbar vertebral segmentation and intervertebral disc localisation
• To exploit novel biomarkers to grade the severity of spine disorder (disc degeneration) based on the quantitative measurements
• To validate the new technologies and test their acceptability and usability at clinical sites (MSK Doctors).

Person Specification and Requirements

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 clinical practice. You should possess a good honours degree in Computer Science, Engineering, Physics, or 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, including (but not limited to) (medical) image analysis, segmentation, surface reconstruction, and machine/deep learning. Evidence of ability to engage in scientific research and to work collaboratively as part of a team, including excellent communication skills in both written and spoken English, is required.

Funding Notes

A fully-funded studentship is available for home and international fees applicants for up to three years, as well as funding for travel and participation in conferences.

How to Apply

To apply for this, please send your CV, cover letter, and personal statement in a ZIP file to Dr Lei Zhang at lzhang@lincoln.ac.uk with the subject as the “PhD Studentship Application”. The personal statement should outline your approach to the project and also explain how your qualifications and experience meet the requirements (about one page). Please include contact details for at least two academic references.

Application Deadline: 28 February 2023

For further information, please contact Dr Lei Zhang (lzhang@lincoln.ac.uk) or Prof Xujiong Ye (xye@lincoln.ac.uk) at the University of Lincoln, or Prof Paul Lee (leep@mskdoctors.com) at MSK Doctors. The successful candidate will receive clinical training and work with an interdisciplinary team at MSK Doctors based in Sleaford, Lincolnshire.

PhD Studentship

Physics-Informed Neural Networks for Gas Turbine Applications

Academic Supervisor: Dr Sepehr Maleki (University of Lincoln)
Industrial Supervisor: Professor Senthil Krishnababu (Siemens Energy)
Co-supervisor: Dr James Taylor (University of Cambridge)

The University of Lincoln is inviting applicants for a fully funded 4-year PhD position in collaboration with Siemens Energy and the University of Cambridge to investigate Physics-Informed Neural Networks for Gas Turbines applications.

Background:

The physical world around us is profoundly complex, and for years, researchers have sought to develop a deeper understanding of how it functions. Building models capable of predicting the characteristics of multi-physics systems continues to be a critical challenge within the sciences. Gas Turbines (GTs) are a class of such systems where Deep Learning models have been applied to address some of the domain-specific challenges. As powerful as they are, neural networks are purely data-driven. Physics Informed Neural Networks (PINNs), on the other hand, are data-driven and can abide by the equations that govern the underlying physics.

Recently, there has been growing research in applying PINNs for predicting flow and heat transfer in relatively simple scenarios. In these applications just by providing the boundary conditions and informing the neural network of the governing Navier-stokes equations, flow parameters are predicted without the need for any mesh or other numerical models. Such simple applications can be extended to industrial scenarios with significant scope for the application of PINNs in gas turbines.

Aim and Scope:

This project aims to investigate the development of PINNs for GT applications and is an excellent opportunity to develop and apply state-of-the-art machine learning techniques to solve real-world problems.

Research Environment:

Your supervisory team will consist of academics from the Universities of Lincoln and Cambridge, as well as an industrial supervisor from Siemens Energy. At the University of Lincoln, you will work within a vibrant and rapidly growing community of PhD students and postdoctoral researchers in the School of Engineering. You will become a member of the Lincoln AI Lab (LAIL) Research Group and have the opportunity to collaborate with colleagues from Computer Science, and other units across the College of Health and Science and beyond as needed. There will be an opportunity to use an experimental rotating compressor rig to help validate and inform the physics-based models.

Funding:

This is a fully-funded studentship, applicable to Home and international applicants. It covers tuition fees and provides an annual stipend at minimum UKRI rates (£17,668 for 2022/23) paid in monthly instalments.

Required Skills:

Applicants must have a First or Upper Second Class honours degree (or equivalent) in Engineering or Computer Science. Candidates with a background or experience in other relevant areas such as physics and mathematics are encouraged to apply if they have evidence of knowledge (e.g., publications) relevant to the skills required for the post (i.e., Deep Learning). Excellent English language communication skills (IELTS score of 6.5 or above for non-native speakers) and the ability to work to deadlines are essential. Knowledge of Fluid dynamics, CFD, FEA, and Gas Turbines are desirable.

How to Apply:

Please apply by requesting an application form PINNsicase@lincoln.ac.uk and returning it alongside a CV, and certified copies of degree certificates and transcripts to PINNsicase@lincoln.ac.uk. The application form includes a personal statement, please use this to outline how your qualifications and experience meet the requirements. Deadline for applications is 31 July 2023, candidates will be notified of the outcome of the shortlisting process by 11 August. All identifying data will be removed from your application prior to being sent for consideration by the shortlisting panel.

PhD Funded Studentship

Reference Number: 1AC-2AU-545066 

Adult Social Work and Social Care

Supervisors: Dr Mo Ray and Mr Chris Erskine

Aims

This studentship will focus on researching aspects of adult social care / social work. We encourage potential applicants to develop a proposal that responds to key and contemporary issues in adult social care. For example:

• Strengths based approaches in adult social work and social care provision
• Experiences of transition (for example, for older people with high support needs)
• Social work practice with people with complex or high support needs (for example, people who self-neglect; people identified as frail; people living with cognitive impairment)
• Developing and delivering care close to home in rural environments

Research Environment

The successful candidate will be part of the Healthy Ageing Research Group (HARG) based in the School of Health and Social Care in the College of Arts, Social Sciences and Humanities at the University of Lincoln. 

Funding

This studentship is funded by Lincolnshire County Council and the University of Lincoln. The studentship will cover tuition fees at the home student rate and an annual stipend of £17,668 per year.

Requirements

Applicants should have a first or upper second-class honours degree or equivalent in a relevant area, and those with a master's degree in social work, social sciences, or sociology are encouraged to apply. Applicants should possess excellent written and English language communication skills, the ability to forge relationships with a wide range of stakeholders and the ability to plan goals and work to deadlines. 

How to Apply

Please email your CV (no longer than two pages) and a one-page cover letter outlining your interests, relevant experience, and proposed research area for the topic to Maureen Young (studentshipscss@lincoln.ac.uk). Applicants invited for interview will be asked to complete an on line application form and prepare a presentation.  Please quote the project ID number in the subject line of the email. 

Please contact Mo Ray (mray@lincoln.ac.uk) for further discussion.

closing date: 18 May 2023

Interviews: 5 June 2023

Start Date: by agreement

Funded MSc by Research: Barcode Metagenomics Molecular Biological Benchmarking and Optimisation For Food Testing

Start Date:October 2023

Duration: 12-16 months

Supervisor: Professor Matthew Goddard

Co-supervisors: Dr Enrico Ferrari and Chris Hudson (Eurofins Food and Water Testing UK and Ireland Ltd)

The University of Lincoln is offering a funded MSc by Research. Funding is provided by Eurofins Food and Water Testing UK and Ireland Ltd and the successful candidate will work closely with the company, including visiting their sites. There may be a possibility of employment with Eurofins after the degree, subject to performance and availability of openings.

Per year, food poising affects over half a million people with a societal cost of approximately £700 million in the UK (1). Current methods to detect food pathogens rely on old agar-plate based methods. This research project will help develop aspects of the use of DNA analyses-based methods to detect and quantify food associated bacterial pathogens (e.g., 2). We are looking for a motivated, self-directed individual that is focused on delivering outputs.

(1) https://doi.org/10.1098/rspb.2022.0400  

(2) https://doi.org/10.1016/j.fm.2021.103878

Entry Requirements

Applicants should have a first or upper second-class honours degree or equivalent in a relevant area. The ideal candidate will have experience with DNA extraction, PCR, and other molecular biology techniques and have experience with data analyses. Experience of short-amplicon DNA sequencing analyses would also be extremely beneficial.

Applicants should possess good report writing and English language communication skills and an ability to work independently to deadlines. The successful candidate must be enrolled full-time. A driving license and access to a car would be desirable.

How to Apply

If you wish to apply for this position please send an email to mgoddard@lincoln.ac.uk with a covering letter explaining why you want to undertake this MSc and your relevant experience, and a full CV (with referees).

Closing Date: 28 August 2023

All applications will be assessed, and we may hold interviews to aid the selection process. Once a potential candidate is chosen, they will be required to formally apply to the University for a MSc by Research position. Please note the University’s entry requirements for an MSc by Research, which are found here: https://www.lincoln.ac.uk/course/mbiresms/.

Funding

The successful candidate will receive a tax-free stipend of £18,622 in total and receive a contribution to their fees of £4,712 in total, which will cover the first 12 months enrolment of ‘home’ fee costs. International students may apply but will need to have the funds available to cover up the balance of the international fee costs before enrolment. Fees incurred after 12 moths will not be covered by these funds.

PhD Studentship

Implementation of GPU-accelerated simulations for real time propagated excited states and applications to organometallic photochemistry

Supervisors: Professor Matt Watkins, School of Mathematics and Physics, University of Lincoln and Dr Joshua Elliott & Dr Sofia Diaz-Moreno, Department of Physical Science, Diamond Light Source Ltd.

4-year Fully Funded PhD Studentship developing real-time time-dependent density functional theory simulations of photoactive organometallic compounds.

Diamond Light Source is the UK’s national synchrotron science facility. By accelerating electrons to near light-speed, Diamond generates brilliant beams of light from infra-red to X-rays which are used for academic and industry research and development across a range of scientific disciplines including structural biology, physics, chemistry, materials science, engineering, earth and environmental sciences.

Summary

Applications are welcome for a four-year funded PhD studentship jointly held at the School of Mathematics and Physics, University of Lincoln and the Spectroscopy Group at Diamond Light Source starting October 2023. The Studentship will focus on developing GPU parallelised routines for Real-Time Propagated Time-Dependent Density Functional Theory with the Open Source CP2K software and their application to Pump and Probe spectroscopy data collected at the I18 Microfocus beamline.

Background

Understanding, on an atomic scale, how light-activated processes drive chemical reaction mechanisms, local geometric rearrangements and charge transfer reactions will be pivotal in engineering next-generation devices and overcoming our overreliance on carbon-positive technology. X-ray pump and probe spectroscopy is a critical tool for probing light-induced reaction mechanisms and photo-excited states. However, this type of experiment typically provides data of seldom observed chemical states and therefore, further analysis and characterisation can be highly challenging.

First-principles simulations can be focal in interpreting experimental spectroscopic data collected at Diamond Light Source. Real-Time Propagation Time-Dependent DFT has emerged as a powerful and viable means to investigate the time evolution of excited states subject to a time-dependent electromagnetic field.

Project Description

The studentship targets the acceleration of the RTP-TDDFT routines within the CP2K code through GPU parallelisation. RTP-TDDFT will be deployed to provide insight into the fundamental dynamical excited state properties of organo-transition metal complexes of particular interest to the facilities’ user communities. In addition, it will implement an automated framework for RTP-TDDFT simulations of more generalised materials across different High-Performance Computing facilities available to Diamond Light Source scientists and users.

Further Information

Diamond Light Source Ltd holds an Athena SWAN Bronze Award, demonstrating their commitment to provide equal opportunities and to advance the representation of women in STEM/M subjects: science, technology, engineering, mathematics and medicine.

How to Apply

We seek a highly motivated student interested in research software development and materials science to join our team. Interested applicants are asked to provide an up-to-date CV and a one to two page cover letter outlining their scientific background, expertise and research interests and the names ad contact details of two references to Joshua.elliott@diamond.ac.uk and MWatkins@lincoln.ac.uk. Informal enquiries are also encouraged.

The position will remain open until a suitable candidate is found.