21 postdoc-computational-fluid-dynamics Postdoctoral research jobs at University of London
Sort by
Refine Your Search
-
for medicine use before and during pregnancy. This postholder would work primarily on a recently funded programme of work to develop a novel approach to understanding and communicating the Safety of Medicines in
-
, dynamic and growing discovery research group as a postdoctoral researcher. The postholder will work in Dr Knuepfer’s group, whose team explores molecular mechanisms of red blood cell invasion by malaria
-
computational methods (Efremova et al. Nature Protocols, 2020; Jain et al., Genome Biology). About You PhD in a biological or computational subject and background in working with genomics data. About the School
-
motivated computational Postdoctoral Research Assistant to lead on an established and successful research line aimed at understanding the genetic events that drive cancer evolution. We have a long-lasting
-
About the Role The purpose of this role is to provide qualitative and quantitative research support for a research and impact programme on food reformulation. This role sits within the Research and
-
About the Role We are looking for a Postdoctoral Research Assistant to work with Dr Chema Martin on a Human Frontiers Science Program Research Grant project entitled “Evolutionary Biophysics
-
responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
-
related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
-
have a PhD and track record in either computer science with specialisation in relevant AI technologies for surrogate modelling, or in Earth or Environmental Science with a strong track record in
-
at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing