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The Kelly lab welcomes individuals with diverse career backgrounds – PhD-level scientists in any discipline with expertise in data and programming, or software engineers outside of academia looking to change
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standard imaging analysis method including use of Python (NumPy/SciPy/PyTorch/Tensorflow), Matlab, C++, version control software (e.g. git), and statistical analysis using R, SQL, etc. Familiarity with
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standard imaging analysis method including use of Python (NumPy/SciPy/PyTorch/Tensorflow), Matlab, C++, version control software (e.g. git), and statistical analysis using R, SQL, etc. Familiarity with
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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in Cambridge. The mission statement of the group is "developing statistical methods to use genetic variation to answer clinically important questions about disease aetiology and prevention". The three
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application of new methods, ensuring they are kept fully up to date with progress and difficulties in the research projects. It is essential that you hold a PhD/DPhil (or close to completion) in a quantitative
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areas (i) Non-adiabatic chemical dynamics, (ii) Physics of charge transport in the solid state, (iii) QM/MM methods, (iv) Atomistic classical simulations of macromolecules (v) Biophysics. You can write
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System Models, or related simulation tools. Additional knowledge of statistical methods, Monte Carlo techniques, or ensemble approaches is highly valued. The post is advertised as full-time. We are open to
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to supervision of MSc and PhD students, and participate in departmental seminars and collaborative meetings. You will also be encouraged to attend and present at national and international conferences, with