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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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. Your Profile: A Masters degreee with a strong academic background in physics, mathematics, computer science, or a related field Proficiency in at least one programming language (Python, C
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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
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related in space and time and to behavioral events. Core Tasks: Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling, or machine-learning frameworks is an asset Strong analytical skills with a solid
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science, or mathematics (or a related field), with a focus on robot vision and control, image processing, or machine learning Solid mathematical and physics background, distinct analytical skills Very good programming
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field Proficiency in at least one programming language (Python, R, C++, Julia, …) Good analytical skills with a sound understanding of data evaluation Prior experience with single-cell data analysis
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, or machine learning Solid mathematical and physics background, distinct analytical skills Very good programming (Python, C++) and computer (Linux, Windows) skills Excellent cooperation and communication skills
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the HDS-LEE graduate school program. Supervise interns and student projects. Your Profile: Excellent Master’s degree in computer science, physics, or mathematics (or a related field), with a focus on