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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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, will analyse the impact of blockades on households, states, corporations and the international order; on the development of political and military strategy; on how the wars were prepared, experienced and
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interconnectedness have fundamentally altered the landscape in which organizations and their projects must now operate. The result is a VUCA world, a term borrowed from military strategy, where Volatility, Uncertainty
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest
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four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. Place of work is Department of Chemistry at Blindern/Gaustad
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. Qualification requirements A PhD degree within neuroscience, psychology, medicine, machine learning or biology or equivalent. Doctoral dissertation must be submitted for evaluation by the closing date
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design and in silico validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning
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with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order manufacturing. This can be proven by the attendance
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approved. Desired qualifications and competencies PhD thesis relevant to the proposed research project Expertise in machine learning Additional expertise in one or more of the following: digital signal
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combine intracranial electrophysiological recordings in humans with behavioral experiments and advanced analytical approaches, including machine learning and statistical modeling. It has two main objectives