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Field
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intelligence/machine learning (or related fields). Job Description: A Post-doctoral Associate in Artificial Intelligence and Quantum Chemistry position is available to work in a project exploring the interface
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specialists in magnetic nanoparticles, water remediation, and CO₂ capture. This position offers an opportunity to develop expertise at the interface of plasma processes, surface engineering, nanomaterials, and
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engineers and technicians, 31 PhD students and 16 postdoctoral researchers. The laboratory's activities lie at the interface between fundamental academic research and industrial research. Over the years, LTM
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institutes for fundamental research in condensed matter physics, with interdisciplinary activities at the interfaces of chemistry, engineering, and biology. The laboratory is affiliated with the CNRS Institute
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nitrides. You will use synchronized HiPIMS processes to tune the properties and performance of the films. The experiments will be informed by in-situ surface and interface analysis. Your work will make use
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project on agentic AI research assistance. ALD for next-generation energy storage technologies: Design and engineer thin films and interfaces of interest in next-generation lithium- and sodium-ion batteries
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cleanroom processes • Overseeing nanofabrication activities within the projects • Training and mentoring PhD students in fabrication techniques and experimental measurements Institut NÉEL is a CNRS unit (UPR
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. Interface with other staff working in related areas including vibroacoustic signal analysis, signal processing, optical fiber sensing, and general instrumentation. Document results in technical reports
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lab members, the group's organoid/tissue-engineered infection models (airway, gut) to prioritize physiologically relevant hits Co-supervise PhD students and contribute to method development, data
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Your position The successful candidate will lead an ambitious interdisciplinary project at the interface of immunology, neuroscience, and cancer biology. The project will investigate how neural