34 parallel-and-distributed-computing-"U"-"Washington-University-in-St" positions at SciLifeLab in Sweden
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General description of the DDLS Fellows programme Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels
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. omics), imaging, electronic health care records, longitudinal patient and population registries and biobanks. The applicant is expected to have a strong computational focus on innovative development and
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, distributed across 15 units at Uppsala University and the Swedish University of Agricultural Sciences (SLU). The central hub of this network is Navet, located at the Biomedical Centre (BMC), which serves as a
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degree in bioinformatics, data science, computer science, scientific computing, or associated field Documented experience with AI methods for analysis of tabular dataset and image-based data including deep
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”. Qualification requirements Required Academic degree in Bioinformatics, Computer Science, Biotechnology or similar. Programming experience, preferably using Python or Javascript. Basic knowledge of version control
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information about us, please visit: the Department of Biochemistry and Biophysics . About the DDLS PhD student program Data-driven life science (DDLS) uses data, computational methods and artificial
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computer science or an equivalent education, such as a Master of Science in Engineering. A minimum of 10 years of documented experience working in IT as a developer, systems specialist, architect, or similar role
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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
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, computer science or a related subject the employer considers of relevance to the position. Experience (3+ years) in working with advanced bioinformatics analyses of omics data from high throughput
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project to study genetic regulatory variation and its link to molecular, cellular and organismal phenotypes using a systems genetics approach. The project is fully computational, and potential approaches