Research Associate/Fellow (Fixed term)

We are looking for a researcher, whose expertise lies in statistical uncertainty quantification or scientific machine learning, to work with Prof. Richard Wilkinson on an ESPRC funded project entitled “Networks of Cardiovascular Digital Twins (CVD-Net)”.  This is a 5-year multi-institutional project with Imperial College, the University of Sheffield, and the Alan Turing Institute, employing 7 postdocs.

The central aim of the project is revolutionise healthcare by harnessing the power of digital twin (DT) technology. Patient DTs are virtual replicas that continuously assimilate patient data into sophisticated mathematical models to provide personalised predictions and inform clinical decisions. This role will focus on developing the statistical/machine learning methodology necessary to create and update models in real time, using clinical data. The focus is on Bayesian methods that are robust, fast, and scalable, and we will work with the other institutions to create a complete workflow from data collection to predictions that can be used in the clinic. The position offers the opportunity to work on cutting edge machine learning methodolgy that will have real world impact.

We believe that talented and inclusive teams deliver the highest quality research and are seeking applications from high quality candidates who enhance the diversity of our existing team.  The School is committed to creating opportunities for people traditionally under-represented in Mathematical Sciences and strives to maintain an environment where people can be their authentic selves.

You will be able to carry out duties to the highest standard and to evidence how through your experience you will:

• Undertake original research of international excellence.
• Develop research objectives and proposals for own and/or collaborative research area.
• Prepare papers for publication in leading journals and/or contribute to the dissemination at national/international conferences, workshops and meetings resulting in successful research outputs.
• Identify opportunities and assist in writing bids for research grant applications. Prepare proposals and applications to both external and/or internal bodies for funding, contractual or accreditation purposes.

We are looking for a confident, organised researcher who can evidence:

• A PhD, or equivalent in statistics, machine learning, or applied mathematics, or who is near to completion of a PhD
• Expert knowledge of statistical methods
• Excellent communication and organisational skills.
• The ability to work independently and as part of a multidisciplinary and multicultural team.
• Networking, actively engaging with and valuing other areas and diverse groups.

This full-time (36.25 hours per week) post is available from 1 October 2024 [or as soon as possible thereafter] and will be offered on a fixed-term contract for a period of 36 months. There is a possibility of an extension, subject to funding. Job share arrangements may be considered.

Many of our team have flexible working patterns so we’re open to discuss flexible working arrangements with you.

If it sounds like you would be a good fit, we look forward to hearing from you.

Informal enquiries may be addressed to Prof. Richard Wilkinson email: r.d.wilkinson@nottingham.ac.uk. Please note that applications sent directly to this email address will not be accepted.

Further details:

• Job Description/Role Profile

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