Research Fellow in Few step Generative Modelling

Research Fellow in Few step Generative Modelling

Full-Time 39148 - 41833 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Explore cutting-edge generative models and conduct impactful research in AI.
  • Company: Join UCL's top-ranked Computer Science Department, renowned for research excellence.
  • Benefits: Access world-class facilities, competitive salary, and opportunities for collaboration.
  • Other info: Inclusive workplace committed to diversity and equality.
  • Why this job: Make a real impact in AI while developing your skills in a dynamic environment.
  • Qualifications: PhD near completion in relevant fields with strong publication record required.

The predicted salary is between 39148 - 41833 £ per year.

UCL’s Department of Computer Science (CS) is a top‑ranked Computer Science Department in the UK. In the 2021 Research Excellence Framework (REF) evaluation, UCL Computer Science was ranked second in the UK for research power and first in England.

This fellowship is funded by the UKRI (EPSRC) AI Hub in Generative Models (grant number EP/Y029763/1). The position is hosted by the University of Cambridge and may require academic visits to UCL’s Centre for Artificial Intelligence. The role involves investigating few‑step generative models such as diffusion, consistency and flow‑based methods, developing stable training and inference techniques, conducting probabilistic modelling research and performing large‑scale computational experiments.

Key responsibilities:

  • Develop deep learning, probabilistic modelling and transformer‑based models for generative AI.
  • Research diffusion, flow matching and consistency models to improve sampling efficiency.
  • Publish papers and contribute to high‑impact scientific outputs.
  • Co‑supervise PhD and master students.

Qualifications:

Eligible candidates should be near completion of a PhD in computer science, information engineering, statistics or a related area, and have a strong publication record. Required skills include:

  • Excellent mathematical and programming abilities.
  • Experience in at least two of Bayesian methods, deep learning, diffusion models, flow matching or transformer‑based models.

Funding and duration:

The fellowship will be funded until 31 October 2028 in the first instance. Appointment will be at Grade 7, or Grade 6B (salary £39,148–£41,833) if the PhD has not yet been awarded, with back‑dating to the date of final submission of the thesis.

Benefits:

The role offers access to world‑class facilities at both UCL and Cambridge, and opportunities to collaborate on large‑scale projects within the AI Hub in Generative Models.

Equality, diversity and inclusion:

UCL is committed to equality of opportunity and to creating an inclusive environment. We encourage applications from candidates from underrepresented groups, including people from Black, Asian and ethnic minority backgrounds, disabled people, LGBTQI+ people and women (for Grade 9 and 10 roles). UCL holds an Athena SWAN Bronze award.

For further information on rewards and benefits or equality and diversity, please refer to UCL’s official web pages.

Contact: genai@ucl.ac.uk

Research Fellow in Few step Generative Modelling employer: WiMLDS Inc

UCL’s Department of Computer Science is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration in the field of artificial intelligence. With access to world-class facilities at both UCL and Cambridge, employees benefit from unique opportunities for professional growth, including involvement in high-impact research projects and mentorship roles. The department's commitment to equality, diversity, and inclusion ensures a supportive environment for all staff, making it an ideal place for those seeking meaningful and rewarding careers.

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Contact Details:

WiMLDS Inc Recruitment Team

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We think you need these skills to ace Research Fellow in Few step Generative Modelling

Mathematical Abilities
Programming Abilities
Bayesian Methods
Deep Learning
Diffusion Models
Flow Matching
Transformer-Based Models

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