52 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"SUNY" PhD positions in Norway
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acquisition to machine learning interpretation to 3D geometry and quality models to machine learning decision support. The PhD project will be carried out in close cooperation with Norwegian tunnelling
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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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and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal clubs, and research
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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
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Technology (NTNU) for general criteria for the position. Desired qualifications Applicants should possess a basic understanding of key AI concepts (machine learning, neural networks, prompt engineering, human
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(predictive) modelling, for example machine learning approaches. Experience in conducting field work in polar or alpine regions. Strong and preferably demonstrated interest in interdisciplinary work at the
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solutions that are practically viable. The successful PhD candidate is expected to carry out research on one or more of the following focus areas: Developing and training robust machine learning surrogates
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outcomes and economic performance, specifically addressing challenges such as overdiagnosis in cancer care. We will utilize economic theory, simulation, economic evaluation and machine learning to quantify
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Norwegian University of Science and Technology (NTNU) for general criteria for the position. Desired qualifications Applicants should possess a basic understanding of key AI concepts (machine learning
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while offering flexibility to tailor your training to specific needs and interests through elective courses and secondments. • Blended Learning Approach: Our training combines intensive in-person