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. Fluency in written and spoken English, as well as strong computational skills, is required. Emphasis will be placed on personal suitability, organizational skills, collaborative work together
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Sweden Application Deadline 4 Sep 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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DNA unit, you are part of a team that includes staff at Stockholm University, and is part of a research environment that includes the Human Evolution research program at Uppsala University and the
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. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-year initiative that focuses on data-driven research, to train and recruit the next generation of life scientists and
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. Proficiency with chemical analytical methods using LC-MS/MS, as well as strong skills in data processing using statistical computing and visualisation (MATLAB, R, Excel, etc.) are also essential. Additional
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of Computer Science and Engineering (CSE)Chalmers University of Technology University of Gothenburg You will be part of the Computing Science Division The appointed candidates will also join a vibrant community of over
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deadline: August 18, 2025 Requirements To qualify for admission to the PhD programme, you must have: A Master’s degree (or equivalent), Completed at least 240 ECTS credits, including at least 60 ECTS credits
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that will be affiliated with one of six possible multidisciplinary projects. The ideal postdocs will have expertise in some of the following areas: computational modeling, computational biology, computational
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agreed upon. Department of Computing Science The Department of Computing Science has been growing rapidly in recent years, with a focus on creating an inclusive and bottom-up driven research environment
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legitimacy. WASP-HS The PhD position is part of the Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS). The vision of WASP-HS is to foster novel interdisciplinary knowledge