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to the above requirements • Strong background in optimization and partial differential equations • Strong background in numerical mathematics and computing • Machine learning skills are welcome • English skills
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, -- mathematical theory for artificial intelligence, -- optimization and numerical computation over manifold, -- systems and control theory, -- algebraic computation theory and cryptography. The position is for two
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the estimation of some of the significant water balance components. The candidate is expected to seek an optimal integration between the physical representations of the various processes and the computing power
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Job description We are recruiting postdocs to contribute to research projects in the Mathematical Modelling of Infectious Diseases Unit, at Institut Pasteur in Paris. The candidates will be expected
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with neuroimaging, numerical mathematics, optimization, inverse problems, software development, motivation and research interests. The location for this research will be the workgroup of Prof. Dr
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skills in programming, modelling, and data analysis. Experience in formulating and solving mathematical optimization problems, as well as working on real-world demonstrators, is an asset. Proficiency in
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. Currently, a number of mathematical models exist to describe the onset and progression of Alzheimer’s Disease and the effects of anti-amyloid therapies on disease progression. The models are informed by a
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. The Department of Mathematics and Computer Science, Hessain.AI research group “Artificial Intelligence – Deep Decision Support Systems”, led by Prof. Dr. Martin Becker, is currently accepting applications for a
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In this Postdoc you will investigate methods for risk-aware navigation among decision-making agents, using learning and optimization, and apply them to self-driving cars and teams for quadrotors
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: Mathematical Modeling: Develop mathematical models to simulate and optimize the entire waste management chain—from collection to treatment. Data Integration and Analysis: Integrate real-time and historical data