Research Assistant/Associate in Machine Learning (Fixed Term)

University of Cambridge; Cambridge; On-site; GBP 32,000 - 46,000; Full time

Job summary

A leading university seeks a Research Assistant/Associate in Machine Learning to join their Machine Learning Group. This fixed-term position focuses on deep generative models to enhance drug discovery and materials science. Candidates should have a PhD or be nearing completion and strong programming skills.

Qualifications

• PhD or nearing completion in a relevant field.
• Extensive research experience and a strong publication record.
• Experience in Bayesian methods, deep generative models, or reinforcement learning is essential.

Responsibilities

• Contributing to the research programme in deep generative models.
• Developing research objectives and proposals.
• Presenting findings and assisting with teaching.

Skills

Mathematical Skills

Programming Skills

Deep Learning

Probabilistic Modelling

Graph Neural Networks

Education

PhD in Computer Science

PhD in Information Engineering

PhD in Statistics

PhD in Chemistry

Job description

Research Assistant/Associate in Machine Learning (Fixed Term)

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Research Assistant/Associate in Machine Learning (Fixed Term)

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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC.

The goal of the programme is to do research in the area of deep generative models, e.g., diffusion, energy based, normalizing flow or transformer-based models. With a focus on the particular domain of molecules. The project will contribute to accelerate the drug discovery process, leading to more economic and effective drugs that can significantly improve the health and lifestyle of millions. The resulting methods are also expected to have an impact in materials science, e.g, by leading to more effective batteries.

The Research Assistant/Associate will join the Machine Learning Group at the Department of Engineering, working with Prof. José Miguel Hernández Lobato and Prof. Mark Girolami and other members of the Cambridge Machine Learning Group (http://mlg.eng.cam.ac.uk/). The project offers also collaborations with other participants of the "AI Hub in Generative Models" (UCL , Oxford, Imperial, Edinburgh, Cardiff, Manchester and Surrey) and with its industrial partners.

Key responsibilities include working on deep learning, probabilistic modelling, deep generative modelling, and graph neural networks.

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 will have or be near to completing a PhD in computer science, information engineering, statistics, chemistry 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 Bayesian methods, deep learning, deep generative models, reinforcement learning, graph neural networks.

Interviews are expected to happen in July 2025. 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. 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: £32,546 - £35,116 Research Associate: £37,174 - £45,413

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

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV), including a list of your publications, and a covering letter 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: [e-mail]

Please quote reference NM46277 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.

Further information

• HR7_Further Particulars
  
  

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Seniority level

• Seniority level

  Internship

Employment type

• Employment type

  Full-time

Job function

• Job function

  Research, Analyst, and Information Technology

• Industries

  Research Services

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