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machine learning have transformed our approach to inverse problems in various fields, notably in medical imaging, enabling a deeper understanding of complex data structures. However, although sophisticated
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of the following topics will be appreciated: · SAT solving, · Problem encodings and reformulation, · Cryptography, · Pattern mining and machine learning. Website for additional job details
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integration. - Basic knowledge of Machine Learning and Machine Learning Operations would be a plus. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UAR6402-CHRDUR-163/Default.aspx Work
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Description The person recruited will be responsible for the development of a computer system that combines deep learning, natural language processing, and psychology of language. - Develop, manage and
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physics or computer science, with a solid background in AI/machine learning techniques. A background in plasma transport phenomena as well as an experience with data analysis, statistical methods, and
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-based drug design Machine learning Molecular dynamics HTS data and SAR analysis Communicate with project teams and other departments: Interact with other experts in the project in various experimental
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detection systems for ICS while considering advanced threats that usually have low activity profiles. We will leverage Machine Learning (ML) techniques, in particular, deep learning algorithms over Graph
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Proportion of work : Full time Workplace : Institut de Science et d'Ingénierie Supramoléculaires (ISIS) – Centre Européen de Sciences Quantiques (CESQ) Desired level of education : PhD or equivalent
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communication skills and team spirit, and an ability to work in autonomy are essential. Fluent English both spoken and written is required. Degree: PhD level in computer science, machine learning, bioinformatics
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Ecole Nationale Supérieure des Mines de Saint Etienne | Saint Etienne, Rhone Alpes | France | 2 months ago
ENFIELD. Mines Saint-Étienne conducts research on sustainable AI from the angle of computational cost of machine learning and lifecycle assessment of AI systems. Scientific challenges: Language models and