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Computational Molecular Medicine, led by Prof Julien Gagneur, develops computational approaches to study the genetic basis of gene regulation and its implication in diseases. Applications of our work range from
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exploiting new chemical biological tools in collaboration with Prof Hartley (School of Chemistry). Techniques used: Biomolecular NMR spectroscopy, Protein X-ray crystallography, Biochemical assays, Chemical
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systems, multi-function radar, AI/ML algorithms rely on high performance digital signal processing and real-time computing to provide high-fidelity results in an actionable timeframe. The ARRC intends
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a
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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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High Speed Stereo Imaging Techniques for Flame Studies School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Y Zhang Application Deadline: Applications accepted
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storage Innovation in the Machine Learning algorithms for EDA in terms of Computational Complexity, Performance Scores, etc. To learn more about our previous work, please check out our website
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Project Self Funded Prof Mohamed Pourkashanian, Prof W Nimmo, Prof Lin Ma Application Deadline: Applications accepted all year round Details In the urban environment at the household to institutional
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MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms