Research Associate in Machine Learning for Drug Discovery

☞ University of Sheffield

View: 118

Update day: 07-05-2024

Location: Sheffield Yorkshire and the Humber

Category: Pharmaceutical / Chemical / Biotech Science Labor

Industry: Education

Job type: Full-time, Contract

Salary: £31,866 - £35,845 a year

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Job content

Job Reference Number:UOS029004

Job Title:Research Associate in Machine Learning for Drug Discovery

Contract Type:Fixed-term with an end date of 31 March 2024

Working Pattern:Full time

Faculty:Faculty of Engineering

Department:Department of Computer Science

Salary:Grade 7
£31,866 - £35,845 per annum

Closing Date:22nd July 2021

Summary:

We have an exciting opportunity at the NIHR Sheffield Biomedical Research Centre and the Department of Computer Science

for someone with a passion for machine learning looking to use their skills in developing transfer learning models to predict

drug response using high-dimensional genomic measurements.

You will join the Bioinformatics and Machine Learning group in our large academic department and world leading research

institute in translational medicine. You will also work in a well-connected team collaborating with pharmaceutical companies,

hospitals and research institutes in Canada, USA, and Europe.

We are seeking candidates with a demonstrable knowledge of a wide range of machine learning techniques, in particular,

probabilistic modelling and practical experience genomic or dose-response data. You will hold a PhD, or have equivalent

experience, in a computational discipline with a solid background in mathematics/statistics, excellent scientific programming

skills and eagerness to contribute to open source software.

If you are passionate about the practical impact of machine learning on healthcare, then we would love to hear from you.

Please see the About the Job for more details.

We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total Reward Package includes a

competitive salary, a generous Pension Scheme and annual leave entitlement, as well as access to a range of learning and

development courses to support your personal and professional development.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions

complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research,

teaching and student experience.

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Deadline: 21-06-2024

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