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for space logistics. With the development of mathematical models and optimisation algorithms, we aim to support strategical, tactical and operational decisions in the context of the deployment of in-orbit
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well as topics in phylogenetics. This project will involve working closely with experimentalists, and will be co-supervised by Prof Gerald McInerney and Dr Daniel Sheward, who have expertise in virology and
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algorithms, AI-driven applications and generative AI, exploring how legal mechanisms can prevent and remedy systemic biases that adversely impact LGBTQ+ individuals and which legal routes are available
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relevant background in algorithms and/or database systems with a research-oriented master’s thesis. Good programming skills. Good written and oral English language skills. Your education must correspond to a
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their validation, how effectively they generalize and extrapolate knowledge, and how might they be improved through transfer learning. You will be supervised along your efforts by Prof. Christof Devriendt
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the development of new algorithms for processing, analysis and inversion of active and passive seismic data and the application of these algorithms to field data. Student type Future Students Faculties and centres
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. The position is hosted at the Chair for Algorithms and Complexity, headed by Prof. Susanne Albers (http://wwwalbers.in.tum.de/index.html.en). The dissertation work will involve research in the fields
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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, storage and demand. YOUR TASKS You will develop mathematical models and metaheuristic algorithms for complex optimization problems in the context described above, see e.g., https://arxiv.org/abs/2503.01325
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. Defining predictive tasks based on clinical goals. Selecting and setting up appropriate data preprocessing pipelines. Training and evaluation of computer vision models. Internal and external algorithmic