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with implementation of: existing algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.]; data management and analysis
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is to discover governing equations from experimental data to generate mathematical models of cellular signaling dynamics. You will help design algorithms for data-driven model discovery, test proposed
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algorithms that allow robots to refine their control strategies based on observed human behaviour. Collaboration: The project will benefit from extending existing collaborations between the University
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research in neuro-symbolic AI, with a focus on using generative and agentic AI, as well as AI standards to create trustworthy information resources. This includes the design of algorithms, tools, and process
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, high school timetabling, examination timetabling, master thesis defense assignments and scheduling and student project and master’s thesis assignments. For these problems, we wish to develop an algorithm
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Manipulation in Cluttered and Dynamic Environments (ID: TUEILSY-PHD20240930-SCMM) A more detailed topic description can be found at https://www.ce.cit.tum.de/lsy/open-positions/open-phd-positions/ . Requirements
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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to various knowledge-based system(s) to simplify code maintenance and to improve support. Develop/change data input, files/database structures, data transformation, algorithms, and data output by using
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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propose to create a central hub that brings together the best and brightest to capitalize on technologies such as machine learning, artificial intelligence, natural language processing algorithms, and