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Field
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of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. SciLifeLab SciLifeLab is an academic collaboration between multiple Swedish universities
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and motivated individual to pursue a PhD in the area of machine learning with focus on explainable clustering. The prospect PhD student will join a research team in KTH led by Professor Aristides Gionis
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. The position is linked to a research project (through WISE and WASP ) in collaboration with experts in both machine learning and artificial intelligence (Pawel Herman at KTH’s Department of Computer Science and
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approaches that combine artificial intelligence, machine learning, natural language processing, and social sciences. This collaborative and cross-sectoral approach aims to produce robust methods for evaluating
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phenotypes, based on both statistics/machine learning and computational mechanistic modelling. The group specializes in analysing complex OMICs datasets (e.g., transcriptomics, proteomics, microbiota
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statistical techniques, machine learning or artificial intelligence in subject area of the position. Connection of the research to infrastructures which Sweden or the department are already involved in. Terms
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chemical and mineralogical analyses (e.g., XRD, SEM, geochemistry). AI and machine learning applications: Develop and apply AI methods to identify patterns and relationships between geophysical parameters
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integrated part of both centres, with focus on new methods for analysing and modelling molecular data, cellular mechanisms and clinical phenotypes, based on both statistics/machine learning and computational
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of heuristic models, mathematical programming, machine-learning and multi-objective optimization. Teaching includes basic and advanced courses within the subject, for example in the international international
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through the application of both well-established statistical modelling and newer machine learning methods. The research specialist will be integrated in the computational team led by John Wallert