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is committed to diversity and equality in education and employment. Qualifications: Minimum Qualifications: PhD in computational biology, machine learning, cancer biology. Four years of experience with
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university’s Norrköping Campus. MIT is an international leader in interdisciplinary, applied research with expertise in visualization, design, computer graphics, and learning science. The research nexus
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leader in interdisciplinary, applied research with expertise in visualization, design, computer graphics, and learning science. The research nexus for the division is the Visualization Center C, a unique
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Université de Lorraine - LORIA : Laboratoire Lorrain de Recherche en Informatique | Nancy, Lorraine | France | 25 days ago
models and algorithms for the efficient dynamic collection of data from a distributed network of sensors when such data are requested by a machine learning process. Firstly, the student will have to
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large data sets. Experience in machine learning techniques would be an asset Knowledge of infectious diseases. Knowledge of diagnostics research would be an asset Proficiency in coding and analysis using
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is expected An excellent mathematical background and experience in software development is required Experience in developing and applying statistical and/or machine learning-based computational methods
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mentality for cutting-edge research in various fields including robotics, machine learning and systems intelligence. An exceptional opportunity to experience research in a highly inspiring international
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goal of this project is to develop machine learning techniques to improve climate change simulations. M²LInES is composed of more than 30 scientists at NYU, Columbia, MIT, Princeton, GFDL, NCAR and CNRS
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. using programs like PLINK, bigsnpr, regenie, BOLT-LMM, GCTA, LDSC, LDAK, LDpred1/2, PRS-CS, SBayesR, PRSice. Machine learning approaches, e.g. deep learning, autoencoders, XGboost, or penalized regression
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analyses using Danish register data and/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning