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chemistry and study the physicochemical properties of peptides loaded into the materials. Build surrogate models and apply machine learning techniques to extract design rules and rapidly screen thousands
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systems are generally ill-conditioned. The project sits at the intersection of classical numerical analysis, scientific machine learning and computational chemistry. Based on regularization techniques and
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 25 days ago
learning, privacy, security and distributed systems. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-10411 Requirements Skills/Qualifications We are looking for a candidate with
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want to develop machine learning approaches that not only withstand adverse conditions but actively learn from their own failures – and can you back those systems with rigorous formal guarantees? We
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SINTEF Ocean; FME Martrans https://martrans.no/ and the Norwegian Maritime AI Centre https://www.ntnu.edu/mai The research is foreseen to focus on fluid-dynamic properties of the different sail types
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. – knowledge of computer vision; knowledge of deep learning architectures; – Knowledge of C++, Python, Matlab; – Analog/digital circuits IC design capability; – Testing of electronic devices and systems; FPGA
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thinking deeply about the results and modelling them. Applicants are not expected to bring expertise in all listed areas; the required methods will be learned during the project. This is a collaborative
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
by LA/P/0083/2020 and 1801P.01460.1.06 LARSYS/ISR BASE 2025-2029 – LASEEB/ISR, financed by national funds through FCT/MCTES Workplan: The scholarship holder will acquire electrophysiological data (EEG
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Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of the Leiden Institute