23 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" positions at University of Stavanger
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collaborations. We seek applicants with strong analytical skills, background in computational fluid dynamics and/or machine learning, and a genuine interest in advancing reliable scientific machine learning
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25th March 2026 Languages English Norsk Bokmål English English Postdoctoral Fellow in educational science with a focus on learning and instruction Apply for this job See advertisement Job
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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academic environment. The person appointed will primarily teach and supervise students at bachelor's, master's and doctoral levels within the subject area, conduct research and disseminate research results
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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the competencies that the Research Fellow will acquire. Access to career guidance will be provided throughout the doctoral education. Research topic We welcomes applications specialising in several fields
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the competencies the Postdoctoral Fellow is expected to acquire during the employment period. The Postdoctoral Fellow will also have access to career guidance throughout the postdoctoral period. Candidate A: One
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resource in the working and learning environment at UiS. When we have different backgrounds and experiences, we can approach challenges from multiple perspectives and find better solutions. At UiS, we
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resilience of bridges under climate change-induced hazards such as flooding, scour, and debris impacts. The research aims to develop advanced numerical models and machine learning tools to predict loads