7 computer-science-intern-"https:"-"https:"-"https:"-"Central-European-University" positions at CEA

  • CEA | Romans sur Isere, Rhone Alpes | France | 17 days ago

    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

  • CEA | Romans sur Isere, Rhone Alpes | France | about 2 months ago

    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

  • CEA | Corbeil Essonnes, le de France | France | 6 days ago

    contribute to the development of leading experimental research in magnetic sensing and spin-electronics-based devices. Main responsibilities: • Develop an original and ambitious experimental research programme

  • CEA | Romans sur Isere, Rhone Alpes | France | about 1 month ago

    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...

  • CEA | Corbeil Essonnes, le de France | France | about 2 hours ago

    in unprecedented ways. She/he will play a central role in directing and carrying out the above program, drawing on the skills present in the two partner groups. More generally, she/he will also benefit

  • CEA | Romans sur Isere, Rhone Alpes | France | 17 days ago

    , 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

  • CEA | Corbeil Essonnes, le de France | France | about 2 hours ago

    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),...

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