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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
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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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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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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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research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world environments. Through strategic partnerships
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University’s distinctive applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world settings. Through strategic partnerships forged by our academic staff with
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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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. 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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approaches such as RNA-seq and ChIP-qPCR/seq. • Experience with fluorescence microscopy. • Proficiency in statistical analysis and/or coding (e.g., Python, R) for data analysis. • A willingness to learn and
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experience with qualitative and transdisciplinary methods, including field research, ethnography and participatory action research Proven relevant teaching experience; experience with e-learning; facilitation