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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
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is possible in collaboration with other members of the lab. Desired (but not absolutely required) skills: programming in python, machine learning, and experience in protein structure analysis. Required
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numerical methods, as well as familiarity with concepts in complex systems, physical memories or machine learning. We strongly believe in the benefits of an inclusive and diverse research environment, and
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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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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML
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Chemistry (experimental/computational physical chemistry) -Transition metal photocatalysts studied by femtosecond X-ray science with a focus on hybrid experimental/machine-learned structural dynamic analyses
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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Job Description Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges? Responsibilities and
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security vulnerabilities. You will innovate the Find2Fix pipeline by making the different steps, including found issues and suggested patches, easier to understand using interpretable AI using state machine