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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | about 1 month ago
compréhension des processus physiologiques au niveau moléculaire et pour l'amélioration des approaches théoriques pour le traitement des maladies. Le groupe de recherche hôte (https://sites.google.com/site
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Description In this project, we develop machine learning models for prediction of optical properties of chiral molecules based on DFT/CCSD data which we calculate ourselves. We include derivative information by
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pipelines that push technological boundaries for our clients. The Engineer will tackle complex challenges at the intersection of Large Language Models, Computer Vision, and Predictive Analytics while ensuring
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intelligence as applied to trauma systems and acute care surgery. Fellows will engage in cutting-edge research spanning multiple domains, including risk prediction models for surgical complications, clinical
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hydrogenation, dehydrogenation, and hydrogen transfer reactions. Detailed characterization and kinetic studies will be performed to test computational predictions and microkinetic models, and to refine machine
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identification, i.e. learning of models from measured data, and iii) real-time control, e.g. using the model predictive approach. We are working on several projects with industrial partners across the energy
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models, performance evaluation criteria, and the grant contract model are those approved under the University of Coimbra's Research Grant Regulations. Where to apply Website https://www.apply.uc.pt
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models. Misclassification Characterization (Month 4-5) Construct an augmented misclassification dataset containing: original and perturbed variants, model predictions, correct labels, perturbation type
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://www.nature.com/articles/s42256-021-00407-x), and extending these tools or developing new models as needed. The candidate will have the opportunity to work directly with experimentalists to validate predictions and
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models