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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical
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data at an internationally competitive level. Experience of biostatistics or machine learning approaches Proficiency in a scripting language like R or Python, as well as ability to work efficiently in a
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-related research experience in multi-omics data integration and statistical modelling familiarity with machine learning methods for biological data experience in data visualization or development of user
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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the department’s activities within method development for machine learning-based computational biology. The duties include supervision of PhD students and postdocs, and teaching at basic, advanced and research level
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You must have: a PhD in microbiology, infection biology, or closely related relevant fields. Preference will be given to applicants who have completed their PhD or attained equivalent expertise