Research Fellow in Few step Generative Modelling
Core
Investigating few-step generative models (diffusion, consistency, flow-based) to develop computationally efficient and theoretically grounded approaches for high-quality generation with reduced sampling cost.
Role type
Research Fellow (Academic)
Builds
Production ML models | research
Domain
Academic research in Generative AI, probabilistic modelling, and deep learning
Deliverable
research
Required skills
probabilistic modelling, deep learning, diffusion models, flow matching, transformer-based models, Bayesian methods, mathematical modelling, large-scale computational experiments
Preferred skills
co-supervising PhD and master students, publishing papers
Technologies
standard machine learning frameworks, publicly available scientific datasets
Responsibilities
developing stable training and inference methods, conducting probabilistic modelling research, performing large-scale computational experiments, co-supervising PhD and master students, publishing papers
Seniority
Senior, hands-on IC (PhD required)
