Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- University of Birmingham
- Imperial College London
- UNIVERSITY OF SOUTHAMPTON
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Nottingham
- CRANFIELD UNIVERSITY
- KINGS COLLEGE LONDON
- King's College London
- Nature Careers
- Queen's University Belfast
- The University of Southampton
- University of Oxford
- Brunel University
- University of Bristol
- ; University of Cambridge
- Birmingham City University
- Cranfield University
- Manchester Metropolitan University
- QUEENS UNIVERSITY BELFAST
- University of Cambridge
- University of Leeds
- University of Liverpool
- University of London
- University of Manchester
- University of Salford
- University of Sheffield
- University of Stirling
- 18 more »
- « less
-
Field
-
Postdoctoral Research Fellow in Machine Learning for Local Energy Communities Salford Business School boasts a vibrant international community. We are pioneers in redefining education and its role
-
agents, including uncertainty quantification at the agent’s level. The project will bring together ideas from Statistics, Probability, Statistical Machine Learning, Statistics and Game Theory and is an
-
computer science, engineering, mathematics or physical sciences area Recent high quality research experience in machine learning/AI, or both, as evidenced by a strong track record of publications in leading
-
embedded AI systems. They will demonstrate a strong track record of high-quality research in machine learning/AI and/or embedded systems, evidenced by publications in leading conferences and journals
-
and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In addition to your research leadership, you will play a
-
experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
-
to the advancement of AI applications in biological sciences. This role presents a unique opportunity to work with pangenomic datasets while exploring the application of Large Language Models (LLMs) and machine
-
annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
-
experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
-
qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in