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internationally recognized, with interests spanning a broad range of research areas - including methods for high-dimensional data and data integration, especially in molecular medicine; mathematical modelling
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25th September 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures PhD
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
-controller. The second direction concerns the development of efficient numerical algorithms for workspace computation and characterization, considering tools developed in the applied mathematics field such as
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. Nonlinear finite element modelling and characterisation Conduct numerical modelling via nonlinear finite element analysis and physics-informed neural networks Materials characterisation for soft, textile and
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statistical model calibration, machine learning and data analysis. The research environment is international and interdisciplinary, with close links between fundamental method development and technically
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activity of individual neurons and mesoscopic population signals. His team develops open software for analysis and data management that forms the technical basis of the proposed project. The PhD candidate
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, energy systems, computational engineering, or a related field Strong background in numerical methods, mathematical modeling, and network simulation or analysis Good understanding of power systems, gas
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Bourgogne (Dijon), to develop and apply mathematical results to better understand the diversity of your corpus and interpret this diversity in terms of networks, thereby shedding light on the circulation
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, high-performance computing, and quantum computing Strong background in mathematics, optimization, numerical methods, or scientific computing Excellent programming skills (e.g., Python, C/C++, Julia
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mathematics, numerical oriented geosciences, or related field with strong quantitative focus; Strong background in machine learning methods such as neural networks and transformers; Knowledge on handling large