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Research Assistant/Associate in Machine Learning (Fixed Term)

University of Cambridge - Department of Engineering

Location: Cambridge
Salary: £33,002 to £46,049
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 17th April 2026
Closes: 15th May 2026
Job Ref: NM49458

Location: Central Cambridge

We are seeking a highly motivated Research Assistant/Associate in Machine Learning to join an interdisciplinary project at the University of Cambridge focused on machine-learning-guided antibiotic discovery. The successful candidate will work under the joint supervision of Professor José Miguel Hernández Lobato (Department of Engineering) and Professor Andres Floto (Heart & Lung Research Institute).

The position forms part of a collaborative research programme aiming to define and predict the "permissive chemical space" for antibiotics in the pathogen Klebsiella pneumoniae, integrating high-throughput experimental measurements with state-of-the-art machine learning and generative AI approaches. The project brings together expertise in microbiology, chemistry, genomics, and artificial intelligence to develop predictive models and computational tools that guide the design of new antibiotics.

The postholder will contribute to the development of predictive and generative machine learning methods that translate experimental measurements of compound retention and metabolism in bacteria into scalable computational models for drug discovery.

The Research Assistant/Associate will join the Machine Learning Group at the Department of Engineering, working with Prof. José Miguel Hernández Lobato, other members of the Cambridge Machine Learning Group (mlg.eng.cam.ac.uk) and Andres Floto and his group members.

Key responsibilities include working on deep learning, deep generative modelling, and molecular design.

Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and networking with colleagues and students; planning and organising research resources and workshops.

Successful applicants must have (or be close to obtaining) a PhD in Computer Science, Information Engineering, Statistics, Chemistry, Biology, or a related area, with extensive research experience and a strong publication record. Excellent mathematical and programming skills are essential, with experience in two or more of deep learning, deep generative models, chemistry, biology.

Interviews are expected to happen in May and June 2026. Applicants are encouraged to guarantee that referees can submit their letters before such date. The interviews will be done via zoom.

Appointment at Research Associate level is dependent on having a PhD in Computer Science, Information Engineering, Statistics, Chemistry, Biology, or a related area. Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

Salary Ranges:

Research Assistant: £33,002 - £35,608

Research Associate: £37,694 - £46,049

Fixed-term: The funds for this post are available for 24 months in the first instance.

To apply online for this vacancy and to view further information about the role, please visit: www.jobs.cam.ac.uk/job/55258.

Please ensure that you upload your Curriculum Vitae (CV), a covering letter and publication list in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.

If you have any questions about this vacancy or the application process, please contact Kimberly Cole, email: div-f@eng.cam.ac.uk.

Please quote reference NM49458 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
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