51 condition-monitoring-machine-learning-"Multiple" positions at SciLifeLab in Sweden
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evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
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are service-orientated, proactive, thorough and able to quickly find solutions to questions and problems. You enjoy managing multiple tasks in parallel and working independently, efficiently and in a
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sciences, and create partnerships with industry, healthcare and other national and international actors. We are now looking for an outstanding candidate with expertise in machine learning, bioinformatics
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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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responsibilities Financial coordination of projects within SciLifeLab in collaboration with multiple universities. Manage budgets, fund allocations, and payment flows Serve as financial contact point for project
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and/or functional imaging or application of computational modeling, machine learning and AI to understand cellular function. At least five years’ experience working within the university system, another
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multiple locations in Sweden. It serves as the bioinformatics platform at SciLifeLab, a national resource that facilitates research in molecular biosciences by offering access to state-of-the-art
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advanced biostatistics/machine learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a “user fee-based” support model. The projects will differ in
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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well as the clinical activities at the Karolinska University Hospital, unique access to international expertise in machine learning, state-of-the-art imaging, diverse patient cohorts, and relevant computational