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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
join forces to push the boundaries of data‑driven discovery. Our mission is two‑fold: to advance fundamental theory in probability and machine learning, and to translate those breakthroughs into high
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approaches beyond standard deep learning methods. Demonstrated experience in academic research (e. g. through publications, conference contributions or comparable scientific achievements). Very good
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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. Desired additional skills: • Independence and decision-making ability; • Openness to new concepts, ease of learning. Job description: • Conducting experimental research in the laboratory
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). Deep learning has been used to perform this mapping from UAV (Batista et al., 2025; Chudasama et al., 2024; Lambert et al., 2025) or satellite imagery (Mattéo et al., 2021), at both very high resolution
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culture. Experience with expression (E. coli, yeast, and mammalian cells) and purification of proteins. Experience with Drosophila Genetics, Dissection, Immunostaining and Confocal Microscopy. Brief
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on the design of AI-based surrogates of large-scale energy system models and will be integrated in the activities of the Nexus-e group with close links to the ETH AI Center and the Reliability and Risk
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team at AMOLF, working on fundamental questions on physical self-learning systems as part of the NWO ENW‑M1 project “How do physical learning systems learn?”. The research position is intended to start