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to develop and implement machine learning/deep learning tools for personalized medicine in cancer by exploiting electronic medical records and medical images in relation to cancer diagnosis and the
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for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2025/postdoc-in-tree-physiology-and… Requirements Research FieldBiological sciencesYears
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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development fund of the department, IT Academy and a recently started project “Smarter use of data via machine learning” and has close ties to the Estonian Centre of Excellence in Artificial Intelligence (EXAI
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 24 days ago
the machine learning community as challenging, high-dimensional testbeds. Notably, the recently developed WOFOSTGym simulator \cite{solow2025wofostgym}, bridging crop modeling and RL, received the Outstanding
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polar orbit, passing near the poles about 15 times per day and regularly observing the CIFAR study region. Its payload - two optical cameras, a thermal camera, and onboard machine-learning capabilities
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biology, epidimological data and AI-driven systems modeling. The successful candidate will develop and apply computational and machine learning approaches to decode the molecular and epigenetic mechanisms
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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Robotics to develop research in the field, e.g., robot design, control and mechatronics; Publication record in robotics and machine learning, e.g., ICRA, IROS, RSS, CoRL, T-RO, CVPR, ICML; Excellent verbal
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ESSENCE: Efficient Self-Supervised Machine Learning for Adaptive Wireless Communication Systems This project investigates self-supervised learning (SSL) for wireless communication systems to improve