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contribution to society. Required Qualifications* PhD in computer science, computational biology, or related quantitative fields. Strong oral and written communication skills Desired Qualifications* Generative
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to teaching activities and provide support to PhD students and research assistants in the programme. You may be a great fit if: You are a skilled statistician with a PhD in statistics, applied mathematics, or a
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of Stavanger invites applicants for a PhD Fellowship in Algebraic and Symplectic Geometry at the Faculty of Science and Technology, Department of Mathematics and Physics. The position is vacant from January 2026
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guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental
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cookie and refresh page to watch video, or click here to open video) PhD position in Quantum Technology There is a vacancy for a PhD Research Fellow in Quantum Technology at the Department of Informatics
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the application form, which is limited in the number of characters it will allow.) Interviews: 4th and 5th of August 2025. About you The successful candidate must hold a PhD in mathematics or computational biology
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of London Allowance. These posts are funded for 3 years in the first instance. Interviews are expected in February 2025. Candidates should either have a PhD or equivalent qualification in Mathematics, should
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School of Mathematics and Physics Full-time, fixed-term position for up to 3 years Base salary will be in the range of $82,057.75 - $109,246.18 + 17% Superannuation (Academic Level A) Based at our
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. We are also looking for a candidate who can strengthen the department’s teaching portfolio in quantum technology in Norwegian. PhD candidates at the Department of Informatics are affiliated with
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data