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Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the interface of machine learning, statistics, probability, and with
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researchers. The centre is internationally recognized, with interests spanning a broad range of research areas in biostatistics, machine learning and epidemiology and numerous collaborations with leading bio
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spanning a broad range of research areas in biostatistics, machine learning and epidemiology and numerous collaborations with leading bio-medical research groups internationally and in Norway. OCBE is a
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is internationally recognized, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods
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will utilize economic theory, simulation, economic evaluation and machine learning to quantify the benefits of advanced diagnostic technologies in reducing overdiagnosis. Competence You must have
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling. The centre has numerous collaborations with leading biomedical research groups internationally and in