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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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/SinglePageApplicationForm.aspx… Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Professional skills Experience in: Reinforcement Learning (RL), Model Predictive Control (MPC
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, redox states, and catalytic intermediates. You will work closely with colleagues spanning protein chemistry, spectroscopy, and computation to iterate design–build–test–learn cycles and to elucidate
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full time for four years. About the project The working environment in KISN provide great opportunities for students to learn across discipline and have access to world leading groups in memory, space
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to your work duties after employment. Required selection criteria You must have a relevant Master's degree in Computer Science, Artificial Intelligence, Data Sciecnce (with a focus on machine learning
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Master's degree in Computer Science, Artificial Intelligence, Data Sciecnce (with a focus on machine learning) or equivalent. Your course of study must correspond to a five-year Norwegian course, where 120
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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. The applicant must have an academically relevant education corresponding to a five-year master’s degree with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second
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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most
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of humanities measured in the number of students. We develop leading academic groups in the action-oriented humanities, as well as innovative and inquiry-based teaching and learning. The Faculty consists of six
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to WAN and inter-domain networking, Excellent command of foundational and applied AI technology, from neural networks, distributed reinforcement learning to agentic AI and recent developments in