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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, solid phase synthesis, and molecular design . We are looking for someone with experience / wish to learn some of the following: complex chemical systems, reaction diffusion, analytical chemistry, chemical
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provide guidance to PhD students where appropriate to the discipline Contribute to developing new computational models, techniques and methods Undertake management/administration arising from research
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science and artificial intelligence concepts and tools to solve complex problems. Candidates will also be developing machine learning techniques and applying them at scale to specific projects with regular
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successful you will have a PhD* (or equivalent professional qualifications and experience) in engineering, mechanical engineering, materials science or a closely related discipline. Experience in the synthesis
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enable early career academics to acquire a strong and well-rounded foundation to support future applications for substantive academic roles at Durham or elsewhere (no guarantee can be given that a
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Fixed Term Contract for 24 months We welcome applications from passionate and skilled post-PhD or experienced researchers in malacology, epidemiology, schistosomiasis modelling, or spatial data