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Field
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The Department of Mathematics The Department of Mathematics strives for excellence in research. The department balances pure mathematical research with mathematical research motivated by
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data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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(Python, C++, etc.). Experience in mathematical programming and/or optimization software such Pyomo, GAMS or similar. Proficiency in process simulation software (Aspen Plus, Aspen HYSYS, etc.). Experience
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Mathematics, or equivalent. Your degree must include a major independent project (minimum 30 ECTS) equivalent to a master’s thesis. Master students can apply, but the master's degree must be obtained and
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6 Sep 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department epartment of Computer Science Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 21 Sep 2026 - 23:59...
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; • mathematical modelling, development of FEM models (preferably using Ansys), and simulations, particularly in the areas of electromagnetics, power losses, etc.; evaluation, verification, and optimization
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. Objectives: Develop mathematical models. Promote the communication and dissemination of the project results. Develop programming and data exploration techniques. 5. Applicable legislation and regulations
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7 Sep 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Mathematical Sciences Research Field Mathematics Researcher Profile First
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7 Sep 2026 Job Information Organisation/Company UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Department Mechanical Systems Engineering Research Field Engineering » Electrical engineering Engineering » Thermal engineering Chemistry » Other Researcher Profile Recognised Researcher (R2) Positions PhD...
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time