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) department. In collaboration with the project team and experts from ANSSI, you will analyze network flow reassembly policies within firewalls and TLS interception tools. Your mission will be to design
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: Marine Biodiversity and ecosystem functioning across spatial, temporal, and human scales”. The overall aim of the project is to acquire knowledge of the principles governing the structure, dynamics
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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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existing structural and functional MRI data, acquire new data in collaboration with clinical researchers, and prepare publications and conference presentations. - Study preparation - Data acquisition (MRI
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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and educational issues with the common goal of contributing to an inclusive, open and resourceful society. Your role The Postdoctoral researcher will be working in the Institute of Teaching and Learning
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the supervision of Dr. G. Pokrovski, within the interdisciplinary ORGMET environment, in collaboration with partner laboratories in Toulouse, Grenoble, Pau, and abroad that possess complementary tools. Short-term
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SD-26085 POSTDOCTORAL RESEARCHER IN THE OXIDATIVE CHEMICAL VAPOR DEPOSITION OF FUNCTIONAL BUILDIN...
Characterisation with state-of-the art methods. · Demonstrated experience in applied research and collaborative projects. · Applicants are encouraged to add their three best publications
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computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material