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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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. Such methods are founded on mathematical models and algorithms from the field of mathematical optimization. The dose planning is based on medical images and a treatment protocol, and it is evaluated by using key
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental
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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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qualifications You have graduated at Master’s level in Molecular Biology, Biotechnology or related subjects or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses
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international conferences or journals, especially publications in the area of Air Traffic Management Knowledge of optimization techniques Knowledge in the area of design and analysis of algorithms Great emphasis
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communication abilities. A solid background in cell biology, neurobiology, tissue models, or in vitro experimental systems is considered a strong asset for this position. Experience with one or more of the
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evaluating efficient and scalable techniques for systems that process and answer such queries (e.g., query optimization algorithms, adaptive query processing approaches). Conducting this research work includes