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period of 12 months, possibly renewable up to a maximum of 36 months, scheduled to start on March 2026. 2. WORK PLAN AND WORKPLACE: The project will investigate the developed algorithms and methods
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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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, evaluate and describe novel algorithms for learning concepts with theoretical guarantees from unstructured data; present research results at international conferences, workshops, and journals; pursue and
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candidate with a background in SAR/InSAR signal processing and time series algorithms, combined with strong expertise from an application domain that strengthens the group’s current activities. Key
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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literacy algorithm to be integrated into an API for health platforms, as well as contributing to the design and testing of the virtual library and associated educational materials. The grant holder will also
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communication skills, and a keen interest in conducting impactful research. For details on how to apply, please see here: https://www.centre-ub.org/studentships/application-process/ Interviews for this studentship
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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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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable