43 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" positions at University of Bergen
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upon by individuals, communities, policymakers and organisations. More information about CET’s research focus can be found on the website https://www.uib.no/en/cet and particularly in the CET Strategy
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are experienced, communicated and acted upon by individuals, communities, policymakers and organisations. More information about CET’s research focus can be found on the website https://www.uib.no/en/cet and
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, and relevance for the AI centre and Work Package 2. The department will host an online orientation meeting for the position 8 January 2026 at 14.00 hrs (CET). Participate at: https://uib.zoom.us/j
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at 14.00 hrs (CET). Participate at: https://uib.zoom.us/j/62281498137?pwd=Qakqii2iD8DxRLgrArHcugDrNKyEbW.1 Project proposal: The artistic research project proposal should place the project in a subject
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modelling tools. This PhD position aims to achieve to develop by the use of automatic picking, rather than manual, travel time picks, and the application of machine learning methods to reliably pick relevant
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position (100%, three years) is related to finance and insurance. The theme of the research project will lie within areas such as: simulation and risk modelling using advanced statistical and machine
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, Language Technology, Computer Science with a specialization in NLP or machine learning, or equivalent. The master's thesis must be submitted before the application deadline. It is a requirement that the
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datasets from laboratory experiments will be provided to support simulation and verification of the resulting model. Replicate and learn a theoretical model for wave and current interaction by posing
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exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with
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solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we