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Field
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systems and control theory, circuit theory, optimization, and machine learning, with the ultimate goal of advancing the mathematical foundations of physics-based learning. Your responsibilities include
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engineering, control engineering, or a related discipline. A strong foundation in at least one of: electrochemical or energy-system modelling, dynamic modelling and control, mathematical optimization, or data
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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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. Good oral and written presentation skills in English. You need strong skills in mathematics, probabilities, optimization and programming. PLEASE NOTE: For detailed information about what the application
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Communication sciences Information science Engineering » Communication engineering Engineering » Electronic engineering Mathematics » Applied mathematics Mathematics » Probability theory Mathematics » Algorithms
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5 Sep 2026 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis Research Field Engineering Computer science Mathematics Researcher
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. Knowledge of renewable energy systems (hydropower, wind, solar) and their grid integration. Experience in mathematical modeling, optimization algorithms, and data-driven methods. Possess a strong academic
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of interest, e.g. the spectral density of strongly correlated models, as measured in ARPES. • The mathematics of the approach will require a lot of innovative developments too. In the only published article
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. This position is ideal for candidates interested in: operations research and optimization, climate adaptation and resilience, sustainable food systems, mathematical modelling and data-driven decision making
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energy transitions at local, national or global scales. Energy Engineering is a part of the Division of Energy Science in the Department of Engineering Science and Mathematics, which is a subject-wide