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Conduct comparative benchmarking and performance analysis against state-of-the-art studies Perform numerical modeling and validation of brain-inspired and neuromorphic algorithms Design, set up, and operate
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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found on the website: https://www.graduateschool-computerscience.de/ . Tuition fees per semester in EUR None Combined Master's degree / PhD programme Yes Joint degree / double degree programme No
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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January 2026 (6pm CET). The deadline for referees to submit reference letters is 14 January 2026 (6pm CET). Please check our website https://www.molgen.mpg.de/IMPRS/application for more details. Tuition
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processing, algorithm design, optimisation and simulation, software engineering and automation and control systems. An overview of the current PhD research projects is given here: https://www.dashh.org
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algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong electronics background, including experience in
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systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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or functional properties. Collaborating closely with experimental partners to integrate decision-making algorithms into real scientific workflows. Publishing results in high-impact machine learning and