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machine learning. This particular thematic area will be supervised by Associate Professor Agni Orfanoudaki. You will be responsible for planning and managing your own research programme within
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engineering, or related disciplines who are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models
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, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation
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-analysis, regression modelling, machine learning). You have a solid basis in at least one common high-level programming language (e.g. R, Python). You enjoy collaborative research in international
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motivated with strong written and oral communication skills. Preferred Qualifications: Experience with first-principles calculations, DFT, Machine Learning, HPC. The individual is expected to actively pursue
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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. Furthermore, the postdoc will aid in
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/or networked microgrids. Brief Description of Duties: The Postdoctoral Associate will assist the Principal Investigator Dr. Peng Zhang in the Department of Electrical and Computer Engineering . The
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development in large multi-site cohorts; and 3) developing advanced machine learning models of task-related brain function. The fellow will have the opportunity to contribute to these projects and will also be
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processing (first, second and multilingual); multimodal language processing and learning; developmental language disorders; educational linguistics and language teaching, testing and assessment; and language
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measurement and control platform for optimal island operation of Chalmers’ wind-battery system. Machine learning-based forecasting tools for renewable production using limited local measurement data