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structures, etc to solve challenging problems is required (there will be a practical coding assessment during recruitment) A solid mathematical foundation is required (multivariable calculus, linear algebra
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. The research tasks require a solid mathematical background with experimental capabilities and excellent programming skills (e.g. Matlab etc.) You should have a strong vision to evaluate and demonstrate
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mathematical statistics (University of Gothenburg / Chalmers University of Technology) Prof. Mats Nilsson, pioneer in spatial genomics (SciLifeLab & Stockholm University) Integration into the national DDLS
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machine learning, engineering, data sciences, applied mathematics, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including
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technology, or applied mathematics, with a specialization in energy systems analysis, or energy economics. Strong written and verbal communication skills in English. The following experience will strengthen
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. To meet the general entry requirements for doctoral studies, you must: Hold a Master’s degree in computer science, image analysis and machine learning, engineering, data sciences, applied mathematics
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background in biology, programming or mathematics is meritorious. Knowledge in medical image processing, image registration, and large-scale analyses of genetic (including Mendelian randomization), protein, or
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for candidates who have: Strong programming skills, particularly in Python Solid analytical and mathematical abilities Experience with machine learning Strong communication skills and proficiency in English The
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departmental duties, up to a maximum of 20% of full-time. Your qualifications You have a Master’s degree in electrical engineering, engineering physics, computer science, applied mathematics or have completed
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, engineering physics, computer science, applied mathematics or have completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses within the topics mentioned above