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exciting opportunities for machine learning to address outstanding biological questions. The PhD student to be recruited will be working on the development of machine learning methods for single-cell data
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intestinal duct section. To achieve this, we will address the inverse design problem using physics-informed machine learning that consists of determining the optimal structure and material distribution
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financement Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/133293 Requirements Specific Requirements Etudiant(e) titulaire d'un Master II en Statistique / Machine Learning
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(I3S), Sophia Antipolis Hosting lab: I3S & INRIA UniCA Apply by sending an email directly to the supervisor: emanuele.natale@univ-cotedazur.fr Primary discipline: Machine Learning Secondary discipline
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innovations, OCTO Technology and the PIMM laboratory at ENSAM are jointly sponsoring this PhD thesis. The research will focus on the application of Physics-Informed Machine Learning (PIML) and Physics-Informed
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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between
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results in leading conferences and journals Required Qualifications PhD in one of the following areas (or related fields): Machine learning / deep learning Quantum computing / quantum information Applied
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of the mines must also be considered. Recent advances in the geotechnical and geomechanical fields have led to a significant increase in the usage of machine learning (ML), thanks to its computational power and
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foundations of feature learning in neural networks. Engage in the life of the CSD, including participating at the weekly seminars. Present results internally and at international venues; Qualifications PhD in