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be part of the IBS/IRIG Institute! The position is available to investigate the enzymatic chemistry orchestrated in the microbial anaerobic carbon cycling. The offer is part of the ERC Project
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Category Chemistry Contract Internship Job title Stage BAC+5 Internship in Materials Chemistry Engineering for a master's degree H/F Subject Electrochemical characterization of Hybrid Solid Polymer
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(or equivalent) in signal processing, applied physics, electrical/electronic engineering, or data science with an interest in physical systems. •Strong motivation for interdisciplinary research, solid foundation
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13 Feb 2026 Job Information Organisation/Company CEA Department IRIG Research Field Physics » Solid state physics Engineering » Materials engineering Physics » Crystal growth Engineering
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Are you passionate about spintronics and nanomagnetism and eager to contribute to scientific excellence? Join the Nanomagnetism and Oxides Laboratory (LNO) at SPEC as a Research Scientist and contribute to the development of leading experimental research in magnetic sensing and...
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, with a possible extension, and be part of the IBS/IRIG Institute! The position is available to investigate the enzymatic chemistry orchestrated in the microbial anaerobic carbon cycling. The offer is
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In the context of ongoing transformations of wireless technologies, the laboratory LS2PR investigates innovative network topologies and digital signal processing schemes. On the one hand, novel network topologies based on distributed MIMO, formerly referred to as cell-free massive MIMO aim to...
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Numerical analysis of fully explicit phase-field models for dynamic fracture of materials H/F Numerical analysis of fully explicit phase-field models for dynamic fracture of materials Understanding material fracture is paramount in the nuclear industry to ensure the structural integrity and...
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potential lends itself to this. Required profile: · With a PhD in physics or mechanical engineering, the successful candidate will have acquired solid skills in the mathematical and numerical methods
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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented in the training data and can naturally arrive during deployment (i.e., a distribution shift),...