697 computer-science-intern "https:" "https:" "https:" "https:" "U.S" PhD positions in United Kingdom
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(at least 5.5 in all sub-skills) or equivalent language qualification. International applicants may require an ATAS (Academic Technology Approval Scheme ) clearance certificate prior to obtaining their visa
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. Reporting to Peter Visscher. To be considered, you must hold a PhD/DPhil in statistical genetics, quantitative genetics, bioinformatics, computer science, statistics, econometrics or another relevant field
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sensory science, psychology, and human–computer interaction, and receive training in experimental design, immersive protocol development, and advanced analytics. Industry engagement includes placements with
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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Project description Electromagnetic (EM) sensing is emerging as a powerful enabling technology for modern high-value manufacturing. Advances in computing power and machine learning now allow us to
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Neuroscience. You may also be considered for this position if you have professional qualifications or experience in engineering. Candidates should demonstrate willingness to build and program their experimental
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art, museum studies, cultural history, technical art history) or be able to demonstrate equivalent experience in a professional setting. The studentship is open to both home and international applicants
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, Computer Science or a closely related field, ideally with a strong focus on quantum computing and numerical simulations. Experience in data-reuploading quantum algorithms, optical quantum computing and quantum
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particularly wide skills training, including experimental and computational work. Applicants should thus have a first (or upper second) class degree in chemistry, biochemistry or a related, relevant subject
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) invites applications for a fully funded 4-year PhD program in Process Systems Engineering. The project aims at integrating data-driven optimisation and design of experiments techniques with model-based