Sort by
Refine Your Search
-
The Kelly lab welcomes individuals with diverse career backgrounds – PhD-level scientists in any discipline with expertise in data and programming, or software engineers outside of academia looking to change
-
on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
-
especially suitable for someone with strong formal reasoning and data analysis skills who is considering progression to a PhD or further postdoctoral research in AI ethics, social choice theory
-
developing formalisms for their interpretation (GMC structure, dynamical state, lifetime, formation, evolution), and/or ii) weighing the supermassive black holes lurking at galaxy centres using molecular gas
-
available data and apply causal inference methods, including Mendelian randomisation, to identify candidate mechanisms linking circadian misalignment and sleep disturbances with cardiometabolic disease
-
in the Department of Chemistry, University of Oxford, for a period of up to 3 years. The project involves the development of methods to use light to regulate transport of amino acids and to engineer
-
discoveries on the electrosolvation force. The project will use a range of optical methods to examine the interactions in colloidal and molecular systems and relate the experimental findings to theories
-
will be educated to PhD level with relevant experience in molecular plant biology and evolution and will work closely with other group members to assist them with gene functional characterisation
-
Institute. This role is especially suitable for someone with strong formal reasoning and data analysis skills who is considering progression to a PhD or further postdoctoral research in AI ethics, social
-
with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly