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development to work under the supervision of Dr Alistair Farley, Scientific Lead for Chemistry, with a dotted line to Professor Timothy Walsh. The position is based at the Ineos Oxford Institute, at the Life
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Inzani. Candidates should hold a PhD in materials science, chemistry, physics or similar. Please include a CV and cover letter in your application detailing your fit to the criteria in the role profile
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About us A post-doctoral research associate position is available at the Photonics & Nanotechnology group, Physics Department, King’s College London, funded by the EPSRC Programme Grant New
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C and python, familiarity with
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C++ and python, familiarity with
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://degradationproject.com/ ) and NEXGENNa (http://nexgenna.org/ ) projects and participation in regular relevant FI meetings. Applicants should hold (or be about to obtain) a PhD in Chemistry, Materials Science, or a closely
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://degradationproject.com/ ) and NEXGENNa (http://nexgenna.org/ ) projects and participation in regular relevant FI meetings. Applicants should hold (or be about to obtain) a PhD in Chemistry, Materials Science, or a closely
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computer codes to solve some of the daily research problems and have experience with high performance computing. You should have a PhD in Chemistry, Physics or Materials Science with a proven research track
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://baldwinlab.chem.ox.ac.uk/. About you Applicants must hold a PhD Biophysics, Chemistry or a related area, or be close to completion, prior to taking up your appointment. The research requires experience in NMR, familiarity
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying