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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
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SCAPIS studies, and by combining data-driven image analysis with multi-omics and genetic analyses of causality. The project will develop and apply image analysis methods that integrate image and non-image
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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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position Eligibility requirements Other qualifications Application A complete application must include: 1. A cover letter summarizing your qualifications, scientific interests and motives for applying (max
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Master’s degree (or equivalent) in electrical engineering, biomedical engineering, engineering physics, or a related field. Strong interest and ability in both experimental work with sensors and data
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, pharmacology and toxicology. Current research in veterinary virology includes both basic and applied research, and spans from virus-host interactions and metaviromics to epidemiology and the effects of viral
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malicious actors. This project will be conducted in the Software Engineering and Security (SES) group. The PhD student will conduct an in-depth investigation of a class of vulnerabilities and devise static
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information about the program can be found at Doctoral studies at the Faculty of Medicine . Background and description of tasks The PhD student will use state-of-the-art methods such as cryo-electron tomography
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that encompasses research units in Chemical Ecology, Resistance Biology and Integrated Plant Protection. Both applied and fundamental research are performed at the department, providing an excellent learning