26 computer "https:" "https:" "https:" "https:" "OsloMet storbyuniversitetet" PhD positions at University of Warwick
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: Semiconductor processing or microfabrication Computational modelling (COMSOL, MATLAB, or similar) Experience with optical/THz systems is beneficial but not essential Curiosity, problem‑solving ability, and
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their skills and interests in this research area to https://warwick.ac.uk/fac/sci/eng/postgraduate/funding/ot_epsrc/app/ via the above 'Apply' button. If this initial application is successful, we will invite
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not essential How to apply: Interested candidates should submit an expression of interest by sending a CV and supporting statement outlining their skills and interests in this research area to https
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of Dr Hannes Houck . The PhD project is funded under the ‘DeCoDER’ programme by the European Research Council (ERC-StG, 101222417 – press release ). The position is available for a start date from
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Fully funded 4-year PhD studentship in Computational Chemistry Supervisor: Dr Zsuzsanna Koczor-Benda, UKRI Future Leaders Fellow (FLF) We are looking for a highly motivated and talented PhD
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A 4-year PhD position is available as part of the EUTOPIA PhD co-tutelle programme, working jointly in the groups of Mark Greenhalgh at the University of Warwick and Frank De Proft and Mercedes
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(UK or worldwide) Research Summary: We are advancing a range of projects focused on chalcogen bonding across synthetic, catalytic, analytical, and computational chemistry. Chalcogen bonding is an
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Centres Research area and project description: Artificial intelligence (AI)-based technologies are being adopted across various sectors at an unprecedented scale. However, the computing resources required
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. The candidate should have a good 2.1 Bachelors, or Masters degree in Electronic Engineering, Computer Sciences or equivalent. Experience in communications and networking, AI, or robotics is desirable but not
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell