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We invite applications for a fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is available to UK (Home) candidates only
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equivalent) in any relevant chemical engineering or science subject.Funding is co-funded through Engineering and Physical Sciences Research Council (EPSRC) and BASF . Project summary The Industry Case (IDLA
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PhD studentship in Machine Learning for Computational Physics and Chemistry, University College London, UK A 3.5-year PhD studentship is available to work under the supervision of Prof Jochen
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desirable Basic administrative skills and some experience is desirable Interest in nuclear weapons, deterrence and the High North is desirable Experience with working in digital or physical archives is
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(including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement) Applicants whose
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. For more information and details of how to apply, please contact Gemma Goldenberg g.goldenberg@uel.ac.uk . The selection process will comprise of an informal chat via MS Teams, candidates shortlisted from
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. Research proposal should be a maximum of 3 sides A4. See guidance on writing your research proposal For queries regarding the application process, please contact: pgr.sst.enquire@citystgeorges.ac.uk City
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desirable Basic administrative skills and some experience is desirable Interest in nuclear weapons, deterrence and the High North is desirable Experience with working in digital or physical archives is
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physical materials and digital sensors that allow families from a range of backgrounds to participate in the co-design of new physical energy conservation technologies for the home Develop functional
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. For more information and details of how to apply, please contact Gemma Goldenberg g.goldenberg@uel.ac.uk . The selection process will comprise of an informal chat via MS Teams, candidates shortlisted from