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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | about 1 month ago
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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packages to estimate variance components and/or in R; a desire to further develop advanced computational, modelling and algorithmic research skills, and utilize these developments into practical breeding
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the upcoming flood type, e.g. heavy-rainfall flood or rain-on-snow flood. As PhD candidate you will compare several machine-learning based algorithms regarding their ability to predict the flood type based
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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead
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to algorithmic decision-making in transport, welfare, policing, and urban planning – exposes the deeply sociotechnical nature of contemporary urban governance. In response to growing concerns about discrimination
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theoretical background for MIMO related research, and have attended courses such as Information Theory, Signal and Systems, Modulation and Detection. Experience with OFDM or single carrier baseband algorithms
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predict food-effector systems. Key Responsibilities • Develop graph-based (multi-)omics analysis algorithms • Benchmark graph-theoretic against graph-ML approaches • Analysis of food-related (multi-)omics
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-centered control paradigms. Design and implement algorithms for shared control between human operators and autonomous systems to improve safety, transparency, and performance in teleoperation. Implement and
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strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. The applicant should furthermore
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international research environment covering a wide variety of research areas, such as algorithms and data structures, machine learning, computer graphics and vision, database systems, artificial intelligence