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of workflows in Python to optimize throughput, reproducibility, and data management. The overarching goal is to create a standardized and scalable platform that accel-erates discovery in stem cell biology and
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. The new system to be developed will be tested and optimized with an existing CargoKite ship prototype in real operation. Previous Work https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10530091 https
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Iterative Algorithms: Optimization and Control.” About the Project The focus of the project is the analysis of iterative algorithms arising from time discretizations of nonlinear evolutions of various kinds
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Researchers: Ph.D. in Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent
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purely motor-powered ships and will be marketable as an independent product. The new system to be developed will be tested and optimized with an existing CargoKite ship prototype in real operation
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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) - Semantic 3D Scene Understanding - Face / Body Tracking, 3D Avatars - Non-Linear Optimization - Media Forensics / Fake News Detection How to Apply: Follow the instructions on our application platform: https
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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(MUCCnet: atmosphere.ei.tum.de ) Optimization of an urban sensor network configuration for greenhouse gas and air pollutant measurements using mathematical and physical assessments Analysis of ground-based
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efficient for it, maintaining and integrating a zoo of graph data systems is a huge challenge and leaves a lot of room for optimizations. Our vision is to explore opportunities to unify core parts of