Research Associate (fixed-term)

We invite applications for a two years fixed-term PDRA position supported by an UK EPSRC grant. 

The PDRA will work closely with project partners at Cambridge University to develop a new statistical machine learning tool to aid pre-operative decision making in MRI based brain tumour resection.

About the role:

The PDRA is expected to possess expertise in data analytics, deep learning and/or statistical signal processing, and to work with the PI to develop new Bayesian machine learning algorithms/models. 

Specifically, the PDRA is expected to communicate with project partner to develop novel contrastive learning and deep learning algorithms/models using PyTorch/Keras, develop Bayesian statistical learning algorithm, implement and test it on MRI imaging data, prototype the approach in the form of Python based software, and generalise it to other MRI-based tumour detection task using publicly available data. 

The PDRA is expected to have image processing experiences and ideally with statistical signal processing experiences.

About the team:

Project Partners of this project are researchers at Cambridge University Hospital and Department of Applied Mathematics. 

The collaboration has lasted for more than 3 years and has led to co-supervision of 3 PhD students and two awarded grants. 

The team has a strong track record in this interdisciplinary area and has collaboratively published more than 10 papers in IEEE TMI, MICCAI, ISBI etc in the past 3 years.

Project background:

Glioma is an aggressive brain tumour with varying survival rates. 

In surgical operations, achieving an optimal balance between reducing recurrence risk and preserving brain function remains a challenging task. 

Current automated segmentation approaches (including deep learning techniques) often employ a deterministic boundary to demarcate the tumour infiltrating area, which can pose difficulties in cases with high uncertainty. 

The objective of this project is to develop a novel statistical machine learning approach and tool that utilises partially labelled clinical data to assist surgeons in brain tumour resection. 

This will particularly advance standardisation and efficiency in pre-operative decision making for magnetic resonance imaging (MRI) based cancer diagnostics and has potential to improve the usability of related tools and techniques by integrating a novel nested statistical contour line approach into deep learning models.  

Wider partnerships:

The Department of Computer Science at University of Bath, known for its research-intensive environment and having achieved a 98% recognition rate as world-leading/internationally excellent in REF2021, provides an excellent environment and resources to support this project. 

The PDRA has the opportunity to collaborate with UKRI CDT in Reliable, Responsible and Transparent AI (ART-AI), which promotes interdisciplinary research in trustworthy AI, supports the project through research collaborations and PhD student projects; Institute of Mathematical Innovation (IMI), which provides additional resources and Research Fellow support on this project.

What we can offer you:

We aim to be an inclusive university, where difference is celebrated, respected and encouraged. We have an excellent international reputation with staff from over 60 different nations and have made a positive commitment towards gender equality and intersectionality receiving a Silver Athena SWAN award . We truly believe that diversity of experience, perspectives, and backgrounds will lead to a better environment for our employees and students, so we encourage applications from all genders, backgrounds, and communities, particularly from under-represented groups, and value the positive impact that will have on our teams. 

We are very proud to be an autism friendly university and are an accredited Disability Confident Leader; committed to building disability confidence and supporting disabled staff .

Find out from our staff what makes the University of Bath a great place to work. Follow us @UniofBath and @UniofBathJobs on Twitter for more information.

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We are constantly seeking to reduce the unconscious bias that enters any assessment process, with the goal of creating an inclusive and equal assessment process. To support this, personal details will be removed from application forms at the initial shortlisting stage.