Research Fellow in Few step Generative Modelling

WiMLDS

Women in Machine Learning & Data Science

• Chapters
• North America: USA
• Atlanta – USA
• Austin – USA
• Baltimore – USA
• Bay Area – USA
• Boston – USA
• Boulder – USA
• Charlottesville – USA
• Chicago – USA
• Columbus – USA
• Corpus Christi – USA
• Dallas Fort Worth – USA
• Denver – USA
• Detroit Metro – USA
• Kansas City – USA
• Los Angeles – USA
• Milwaukee – USA
• Minneapolis – USA
• North Carolina – USA
• NYC – USA
• Omaha – USA
• Pittsburgh – USA
• Salt Lake City – USA
• San Antonio – USA
• Seattle – USA
• Syracuse – USA
• Vermont – USA
• Washington, DC – USA
• North America: Canada
• Calgary – Canada
• Edmonton – Canada
• Halifax – Canada
• Montreal – Canada
• Ottawa – Canada
• Toronto – Canada
• Vancouver – Canada
• Africa
• Abuja – Nigeria
• Accra – Ghana
• Algiers – Algeria
• Gaborone – Botswana
• Casablanca – Morocco
• Dakar – Senegal
• Khartoum – Sudan
• Kinshasa – DRC
• Lagos – Nigeria
• Nairobi – Kenya
• Kampala – Uganda
• Vacoas-Phoenix – Mauritius
• Yaoundé – Cameroon
• Asia
• Amaravathi – India
• Bengaluru – India
• Bhopal – India
• Chennai – India
• Delhi – India
• Goa – India
• Hyderabad – India
• Indore – India
• Jaipur – India
• Manila – Philippines
• Mumbai – India
• Mysore – India
• Pune-India
• Singapore – Singapore
• Surabaya – Indonesia
• Tokyo – Japan
• Ulaanbaatar – Mongolia
• Visakhapatnam – India
• Middle East
• Ankara – Turkey
• Cairo – Egypt
• Istanbul – Turkey
• Riyadh – Saudi Arabia
• Tel Aviv – Israel
• Europe
• Amsterdam – Netherlands
• Berlin – Germany
• Brussels – Belgium
• Bucharest – Romania
• Copenhagen – Denmark
• Cambridge – United Kingdom
• Dresden – Germany
• Dublin – Ireland
• Galicia – Spain
• Limassol – Cyprus
• London – United Kingdom
• Madrid – Spain
• Milan – Italy
• Munich – Germany
• Paris – France
• Poznan – Poland
• Sophia – Antipolis-France
• Trojmiasto – Poland
• Zurich – Switzerland
• South America
• Buenos Aires – Argentina
• Cochabamba – Bolivia
• La Paz – Bolivia
• Rio de Janeiro – Brazil
• São Paulo – Brazil
• Oceania
• Melbourne – Australia
• Sydney – Australia
• Press
• Blog
• Contact
• Events
• Open Source Sprints
• Berlin Scikit Sprint 2020
• Nairobi Scikit Sprint 2019
• NYC Scikit Sprint 2019
• Bay Area Scikit Sprint 2019
• NYC Scikit Sprint 2018
• NYC Scikit Sprint 2017
• Hackathons
• Smart Cities
• Healthcare
• Jobs
• Job Listings
• Post a Job
• More
• Leadership
• Code of Conduct
• Become a Sponsor
• Donate

Research Fellow in Few step Generative Modelling

Research Fellow in Few step Generative Modelling

• Full Time
• Hybrid between University of Cambridge Department of Engineering / remote / visits to UCL
• Posted 5 days ago

Website

University College London

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). This hub is a UK-wide research initiative dedicated to developing the next generation of Generative AI models. It seeks to transform science, industry, the economy, and society by uniting leading experts from academia and industry to collaborate on impactful, large-scale projects that no single organisation can deliver on its own. There is an expectation to proactively engage and collaborate with other Hub members and partner institutions, engaging with central Hub activities, cross-theme initiatives, and events.

Successful applicants will investigate few-step generative models, including diffusion, consistency, flow-based, and related probabilistic methods, with the aim of developing computationally efficient and theoretically grounded approaches that maintain high-quality generation performance while reducing sampling cost. The work will involve developing stable training and inference methods, conducting probabilistic modelling research, and performing large-scale computational experiments using standard machine learning frameworks and publicly available scientific datasets.

This post will be funded until 31st October 2028 in the first instance.

Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Grade 6B (salary £39,148 – £41,833per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD Thesis.

About The Role

This unique position is part of a strategic collaboration between University College London (UCL) and the University of Cambridge under the AI Hub in Generative Models. The successful candidate will benefit from the world-class facilities and expertise at both institutions, working under the joint supervision of Professor José Miguel Hernandez Lobato and Professor David Barber. The position is hosted by the University of Cambridge and may require academic visits to the UCL Centre for Artificial Intelligence to support a co-supervision model.

Key duties and responsibilities include working on deep learning, probabilistic modelling, transformer-based models, diffusion models, flow matching or consistency models, publishing papers and co-supervising PhD and master students.

About You

Successful applicants will have, or be near to completing, a PhD in computer science, information engineering, statistics 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, diffusion models, flow matching or transformer-based models.

A job description and person specification can be accessed at the bottom of this page. To apply for the vacancy please click on the ‘Apply Now’ button below.

If you have any queries regarding the vacancy or the application process, please contact the AI Hub in Generative Models ***email_hidden***.

What We Offer

As well as the exciting opportunities this role presents, we also offer great benefits. Please visit to find out more.

Our commitment to Equality, Diversity and Inclusion

As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent.

We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce.

These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women. Our department holds an Athena SWAN Bronze award, in recognition of our long-term commitment and ‘beacon’ status in advancing gender equality.

We are in the process of applying for an Institute of Physics Inclusion Award. You can read more about our commitment to Equality, Diversity and Inclusion here:

To apply for this job please visit

Post navigation

Postdoctoral Scholarship in Geometric Deep Learning / Scientific Machine Learning

AI Studio Coach

Copyright 2026 WiMLDS Theme: Flash by ThemeGrill. Proudly powered by WordPress