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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 20 hours ago
(e.g. geometric deep learning, generative models, representation learning) to decode molecular physiology, pathology, and therapeutic design? Are you passionate about high-throughput biology and scalable
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. (2) Prof. Francesca Grisoni (https://molecularmachinelearning.com/ ) leads the Molecular Machine Learning Group at the Technical University Eindhoven and will lead a project on designing potent cyclic
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to sustainable alternatives. The European Green Deal and the REPowerEU Plan highlight the urgency of decarbonizing Energy-Intensive Industries (EIIs) while ensuring energy supply resilience. However, many
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Engineering or related fields; ii) Suitability of the candidate's training and experience to the objectives of the work to be developed; iii) Experience in projects in the area of Agriculture 4.0 3. Work Plan
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to translate fundamental mechanistic understanding into broadly applicable strategies for catalyst and electrochemical system optimization. The PhD student will work on the design, characterization, optimization
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," part of the Observatory of Higher Education Innovation, which brings together researchers and PhD students from different countries. As a member of the group, the candidate will contribute to joint
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microscope, specifically optimized for spin-wave spectroscopy. Three patents have been filed on the original design, and the setup has already demonstrated unsurpassed spectral resolution. This project is
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. Research groups This project will be conducted within Tim Coopmans’ research group at QuTech, the quantum research institute of Delft University of Technology. The group focuses on the design of quantum
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Max Planck Institute of Colloids and Interfaces | Potsdam, Brandenburg | Germany | about 1 month ago
) for Functional Material Design in Summer 2027! We are recruiting doctoral candidates to help shape a resource-efficient future. The candidates will work on individual projects exploring how we can learn from
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: Developing a system-theoretic framework that models learning as the feedback interconnection between continuous-time circuit dynamics and optimization algorithms. Designing novel energy-based learning