567 algorithm-development "https:" "Simons Foundation" Postdoctoral positions in United Kingdom
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holder will be responsible for the day-to-day running of this programme of work as part of Dr Chiari’s research team within the Cells, Organisms and Molecular Genetics (COMGen) group (https
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of researchers to develop new heterogeneous catalysts for conversion of CO2 to light olefins using high-throughput experimental techniques and AI tools. You will have a PhD in chemistry or chemical engineering, a
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The INEOS Oxford Institute for Antimicrobial Research (IOI) is seeking a Postdoctoral Research Associate in Biochemistry to join an ambitious research programme focused on the development of new
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for a project on histone variants in hyperthermophile archaea. The Warnecke lab study the evolution of chromatin from a comparative perspective, with a particular focus on less-studied organisms such as
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, University of Oxford. This is a 24-month, fixed-term position, funded by the Simons Foundation. The start-date for this position is flexible, but September-November 2026 is preferred. The project will focus
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Institute, University of Oxford. This is a 24-month, fixed-term position, funded by the Simons Foundation. The start-date for this position is flexible, but September-November 2026 is preferred. The project
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team within the Cells, Organisms and Molecular Genetics (COMGen) group (https://www.nottingham.ac.uk/research/groups/cells-organisms-and-molecular-genetics/people/index.aspx ). The role will primarily
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Associate in Biochemistry to join an ambitious research programme focused on the development of new treatments for tuberculosis. This position forms part of the recently funded Oxford TB Grand Challenge
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of pursuing external funding. Experience of computational chemistry techniques. Experience in cheminformatics, machine learning and/or algorithm development for chemical synthesis. Experience with UNIX and HPC
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develop throughput-optimal entanglement distribution algorithms (both centralized and decentralized algorithms) for quantum networks with resource constraints. The project is funded by the EPSRC AI hub