128 parallel-computing-numerical-methods Postdoctoral research jobs at University of Oxford
Sort by
Refine Your Search
-
Listed
-
Field
-
Telescope. You will also have the opportunity to teach. You should hold a PhD (or close to completion) in a relevant area of astrophysics or physics. Experience in performing numerical simulations in
-
used in our work centre around optical imaging and spectroscopy and nanofabrication. The work also relies on theory and simulation, specifically focusing on numerical mean-field electrostatics
-
programme grant with partners across the UK to facilitate the use of hydrogen for aviation, and in particular the icing vulnerability of heat exchangers and parts of the airframe. You will work to generate
-
with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
-
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
-
We are seeking a full-time Postdoctoral Research Assistant in Computer Vision to join the Visual Geometry Group (Central Oxford). The post is funded by ERC and is fixed-term for 2 years with a
-
and manipulation and a knowledge of relevant statistical methods. You will possess exceptional organisational skills, an ability to work efficiently with collaborators and to supervise and educate
-
We are seeking five full-time Postdoctoral Research Assistants to join the Computational Health Informatics Lab at the Department of Engineering Science, based at the Institute of Biomedical
-
hepatitis and liver disease. This post is funded by the National Institute for Health and Care Research (NIHR) as part of a significant research programme that leverages large-scale healthcare datasets
-
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