Research Fellow in Machine Learning, Deep Learning and Artificial Intelligence (Fixed term)

We are seeking a research fellow to take a leading role in research projects related to infection and antimicrobial resistance.

The aim of this research is to understand the emergence, spread and transmission of drug-resistant pathogens in the Agri-tech/health sector (e.g. farms, environment, hospitals, community), with a potential transfer to the human population. We aim to improve diagnostic capabilities for detecting infections and antimicrobial resistance to support treatment selection and the development of novel solutions for better surveillance. This will be done through the development and implementation of big data mining and machine learning-powered solutions for monitoring and diagnostics of infection and resistance. Our analysis will also target another important aspect linked to antibiotic-resistant infections: gain a better understanding of the complex genetic repertoire, molecular interaction networks and pathways underlying resistance (i.e. the resistome) to broaden the possibility of discovery of novel therapeutic targets. To this aim, we will use artificial intelligence, bioinformatics and microbiology to identify new potential druggable targets that may render the microbe susceptible to antibiotics when blocked. Next, and utilizing other learners, we will identify drugs that can block these targets. The successful candidate will work closely with an interdisciplinary team of academics at University of Nottingham, Ningbo Campus, China and industrial partners in the UK and China and industrial partners in the UK and China. 

The Applicant must have, or be very close to completing, a PhD in machine learning, computer science, engineering, mathematics, statistics, physics, or other relevant fields. The candidate must have knowledge and experience in machine learning, deep learning and artificial intelligence techniques. Experience in heterogeneous, complex large-scale data, including sequencing, sensor, biological and imaging data would be desirable. Research experience in applying such methods in antimicrobial resistance, metagenomics, bacterial infections, food and health-related issues and expertise in cloud-based environments would be relevant. Applicant must be able to demonstrate strong programming skills in Python, Matlab, R or other equivalent. Evidence of publications in any of the listed fields. The applicant must also be able to demonstrate research ambition through timely publication of research, coupled with commitment to the research project as part of their on-going career development. 

This position is offered on a fixed term basis until 31 May 2025. Hours of work are full-time (36.25 hours), however applications are also welcome from candidates wishing to work part-time (minimum 18.13 hours per week). Please specify in your application if you wish to work part time and the number of preferred hours. Job share arrangements may be considered.

Requests for secondment from internal candidates may be considered on the basis that prior agreement has been sought from both your current line manager and the manager of your substantive post, if you are already undertaking a secondment role.

Informal enquiries may be addressed to Tania Dottorini, email [email protected] . Please note that applications sent directly to this email address will not be accepted.

It is a condition of this post that satisfactory enhanced disclosure is obtained from the ‘Disclosure and Barring Service’.

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