63 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions at University of Oslo
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public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest in creative or artistic applications. Documentary evidence would be beneficial
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova
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quantitative analyses or master game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside
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/machine learning skills The candidate’s research proposal must be closely connected to the call and the research of NCEI Excellent skills in written and oral English Personal suitability and motivation
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of Oslo that spans molecular, cellular, and systems-level approaches. Starting date not earlier than 1 July 2026. Qualification requirements A PhD degree within neuroscience, psychology, medicine, machine
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analytical approaches, including machine learning and statistical modeling. It has two main objectives: Develop a cognitive task for assessing neurophysiological processes involved in declarative memory
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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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within neuroscience, psychology, medicine, machine learning or biology or equivalent. Doctoral dissertation must be submitted for evaluation by the closing date. Appointment is dependent on the public
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validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning physics, neuroscience and