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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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plus Education and training PhD in Bioinformatics or in Biology, Machine Learning, Statistics, Physics, Mathematics, Chemistry or related areas Languages: Highly proficient in both spoken and written
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Internet Exchange (AMS-IX), and the Faculty of Science of the University of Amsterdam. About Research group The CWI Machine Learning research group focuses on how computer programs can learn from and
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, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research potential at the international level
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simulations using, e.g., COMSOL, Lumerical, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research
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tools are need during the development of new imaging and sensing systems. With the rapid deployment of data-driven methods, repliable uncertainty quantification remains a big challenge that requires
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your