Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of Oxford
- University of Oxford;
- University College Cork
- AALTO UNIVERSITY
- Durham University
- King's College London
- University of Liverpool
- Aston University
- Queen Mary University of London;
- UNIVERSITY OF VIENNA
- MAYNOOTH UNIVERSITY
- Nature Careers
- University of Bath
- Cardiff University
- Heriot Watt University
- Imperial College London
- King's College London;
- Plymouth University
- RCSI - Royal College of Surgeons in Ireland
- The University of Edinburgh;
- University of Cambridge;
- University of Canterbury, New Zealand;
- University of Lincoln
- University of Lincoln;
- University of Liverpool;
- 15 more »
- « less
-
Field
-
and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning
-
fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
-
developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
-
modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
-
and machine learning models. To be successful in this role, you will have excellent communication skills and written English, strong quantitative and analytical skills, the ability to work creatively
-
their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7 master
-
operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer
-
functional genomics, bioinformatics, and machine learning to support the generation, interpretation, and screening of large-scale experimental and computational outputs. The successful candidate will
-
science, engineering, or a related discipline, with significant postdoctoral research experience. The ideal candidate will have strong expertise in computational biology, machine learning, and quantitative analysis
-
machine-learning-based approaches, and to evaluate the thermodynamic costs of quantum operations. You should work effectively as part of a team and engage constructively with collaborators. You will hold a