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(postdoc) Limited contract until: 30.09.2030 Job ID: 6083 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re
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a sequential decision-making process explored through computational simulation and deep multi-objective reinforcement learning. The project will investigate a simulation platform that reproduces
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position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Job description:Staff Scientist / PostDoc – Machine Learning for Scientific Applications You
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and
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patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference to identify strategies for restoring immunological homeostasis. Together
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution
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(postdoc) Limited until: 30.09.2030 Reference no.: 6045 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re
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, Chemistry, Physics, Applied Mathematics, Materials Science, Chemical Engineering, or a related technical field. Demonstrated research experience in machine learning or deep learning for scientific
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etc. We expect excellent skills in mathematics, especially knowledge in formal methods, stochastic systems and processes, the foundations of deep learning. Experience coding with deep learning libraries