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multi‑omics data. You will also partner with AI experts to integrate predictive models and advanced analytics into omics workflows. You will work in an expanding team led by Dr. Masoomeh Rahimpour
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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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research fellows to join a multi-year research initiative sponsored by the Bezos Earth Fund . This project aims to develop and deploy advanced AI-driven learning, prediction, and decision-making tools
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Assistant Professor - Information Systems, Operations Management, Supply Chain Management, and Busin
, natural language processing , computer vision , predictive analytics , and optimization . Experience with generative AI, large language models, automated decision systems, or AI ethics and governance is
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are analysed using big data and business intelligence applications to monitor tourisms. Additionally, predictive modeling methods are applied to estimate tourist mobility behavior and movement patterns between
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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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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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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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/10.1007/s11142-024-09822-y . He, L.- Y., Wang, L. (2025). Can artificial intelligence curb greenwashing? Firm-level evidence based on large language model. Energy Economics, 152, 108954. https://doi.org