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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 months 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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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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to explore and develop AI algorithms, frameworks, and hardware architectures for efficient edge deployment in vehicles, with a focus on neuromorphic computing. You will be part of the scientific TUM HN Team
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, internationally oriented center for cutting edge research on neurodegenerative diseases. We are seeking a highly motivated and skilled Postdoctoral Researcher to develop new algorithms for multi-omics data
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part of change Driving innovative AI and robotics research Development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Implementation of deep learning and
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) • Contributing to analyses of agency, responsibility, trust, mental privacy, and algorithmic bias in neuroAI systems • Collaborating with technical partners on issues of transparency, interpretability, and
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optimisation of analytical and numerical models for metasurfaces Documentation of the algorithms developed and results What you contribute You are enrolled in a Master's programme, e.g., computer
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, and benchmarking of novel algorithms on synthetic and real-world datasets Development and maintenance of open-source research software and reproducible workflows Collaboration with CRC partners across
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the results In addition, you will use algorithms to extract surface characteristics such as roughness, waviness, and contact ratio from the collected data Furthermore, you will develop a machine learning model