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, aiming at improving the information extraction and the of icy planetary bodies subsurface by radar sounder data processing. The selected candidate will be expected to develop novel machine learning methods
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segmentation, target detection and change detection along and across multiannual series of data. Methodologies like foundational models, machine learning, deep learning, multitask learning, enforcement learning
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efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices. Still, many challenges are ahead
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for more than 12 months in the 36 months immediately prior to your recruitment. Skills: Strong interest in AI/Machine Learning, Bayesian modeling and decision-making. Benefits Competitive Salary: Living
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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant
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Science, Telecommunications, Applied Mathematics, or related fields; Solid background in probabilistic modeling, Bayesian inference, information theory, and/or machine learning; Experience with signal processing or decision
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; Distributed multi-agent sensing and cooperative positioning algorithms; Machine learning and data-driven methods for ambient awareness. Working Environment: The PhD will be conducted at the University