25 phd-in-mathematical-modelling-of-biochemical-reactions Postdoctoral positions at University of London
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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
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guiding the development of advanced impact-resistant materials. The main purpose of this role is to develop multi-physics computational models and reveal liquid-solid impact damage mechanisms. The post will
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also have or be close to completing a PhD in any of the following areas as well as the will and commitment to learn relevant topics from the other areas: Statistical and machine learning, mathematical
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reasons for CIN remain unclear. This project aims to track the molecular and biochemical changes that control chromosome segregation accuracy. The findings will expand our basic understanding of mitosis and
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In Vitro Predictive Models to Explore Tendinopathy”. The project is funded by the Medical Research Council (MRC) and part of the organ-chip research work underway within the Centre for Predictive in
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project investigating mechanosensing in Diptera. This post will focus on using detailed wing geometry models and kinematic measurements in computational fluid and structural dynamics simulations to recover
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, considering genetic and environmental risk factors and their interplay. About You A successful applicant should have, or expect to soon receive, a PhD in psychology, psychiatry, bioinformatics, human genetics
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and Immigration website . Full-Time, Fixed-Term (36 months) We are looking for a highly motivated early career researcher with a PhD (or near completion) in psychology, life sciences, genetics
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infrastructure enables recruitment of 200-300 severely injured patients annually as part of the ACIT study. We also have a well-established experimental modelling group with full ethical approvals in place for all
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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