Warning messageCambridge-based members of C2D3 can log in to view more information about this opportunity.
Research Assistant/Associate in Machine Learning x 2 (Fixed Term)
Department of Engineering, Central Cambridge, Cambridge
We are seeking two highly creative and motivated Postdoctoral Research Assistants/Associates to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. All positions will involve research in collaboration with Zoubin Ghahramani, Carl Rasmussen, Richard Turner, Hong Ge and other members of the group (http://mlg.eng.cam.ac.uk/).
The goal of the programme is to develop novel theoretical foundations and practical algorithms for machine learning that will enable 1) towards human-like learning from less labelled data, and 2) robust decision-making and control in uncertain environments. These new methods will be tested on a range of tasks drawn from computer vision, natural language process and reinforcement learning etc.
Key responsibilities include working on deep learning, probabilistic modelling, approximate inference, reinforcement learning, computational neuroscience, sparsity in neural networks, computer vision, Bayesian optimisation, Markov-chain Monte Carlo, deep learning for structured data (including graph neural networks), deep learning for combinatorial optimisation, few-shot learning etc.
Additional responsibilities include working on developing research objectives and proposals; presentations and publications; assisting with project management; assisting with teaching and supervision; liaising and networking with colleagues and students; planning and organising research resources and workshops.
The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science.
- Supports and connects the growing data science research community
- Builds research capacity in data science to tackle complex issues
- Drives new research challenges through collaborative research projects
- Promotes and provides opportunities for knowledge transfer
- Identifies and provides training courses for students, academics, industry and the third sector
- Acts as a gateway for external organisations