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Field
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
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multi-fidelity modelling, your research will advance and integrate three core elements: (i) physics-based multi-fidelity structural models enabling high-resolution analysis at fatigue-prone hot-spots
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24 Jun 2026 Job Information Organisation/Company CNRS Department Biomécanique et Bioingénierie Research Field Engineering Physics » Acoustics Engineering » Materials engineering Researcher Profile
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theoretical modelling. The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually ensure
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of processing routes for advanced ceramics and surface treatments, with the aim of developing materials and components with tailored properties for demanding applications. SAFRAN is a leading French international
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/ProvinceBucharestCityBucharestStreetPiata Romana no 6Geofield Contact State/Province Bucharest City Bucharest Website https://resurseumane.ase.ro/just-circle-measuring-and-advancing-circular-business-models-for-a-fair-and-sustainable
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, verification and control of LLM-based agents AI for materials discovery – generative models, active learning and autonomous discovery of novel materials We welcome applications from candidates whose interests
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on the solid application of scientific research method. The department has a pluralistic approach to research methods that includes, for example, formal theoretical models, quantitative empirical methods such as
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to the deep ocean. These canyons are the conduits for transport of land-derived materials to the ocean floor in avalanche-like events called turbidity currents. Turbidity currents transport enormous amounts