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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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, motivation, and the applicant's personal eligibility. We can offer: A stimulating and professionally challenging work environment. Gross annual salary of NOK 568 700 for a full-time position (code 1017
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will be and what theories and methods you will apply. It must be clear from the application in what way the postdoctoral project will add to your competence and scientific development. The motivation
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, collaborating across disciplines to tackle fundamental challenges through innovative methods, theory and critical analysis. The fellowship period is 3 years. Starting date as soon as possible and upon individual
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), relevant theory and a preliminary plan for data collection (source, methods and statistics) an account of expected outcome, preferably with verifiable hypotheses a time schedule list of partners
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17.515, code 1017 PhD Research Fellow, NOK 555 800 gross salary per year. A compulsory pension contribution to the Norwegian Public Service Pension Fund is deducted from the pay according to current
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. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining
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benefits in the State Pension Plan Opportunity for physical activities within working hours Salary PhD Research Fellow (code 1017): NOK 550 800 a year. Further promotion will be based on time served in
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consisting of both control theory (or related fields) and economics (or related fields). Applicants must have experience in one or more of the topics: Model-predictive control Numerical optimization
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internationally. The group also seeks to contribute to knowledge production in interdisciplinary democratic theory, innovative methodological and democratic experiments, and to maintain a close collaboration with
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the Research Council of Norway. In this project, we will use advanced time-lapse imaging, numerical simulations, and reactive mixing theories to better understand and predict the role of fluid mixing as a driver