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Research. The project is in close collaboration with the Computer Graphics Laboratory at ETH Zürich (Prof. Barbara Solenthaler PhD) who develops a fully virtual infant face and head model. The project will
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relating to computer vision, machine learning and data infrastructure management. Part (50% effort) of this position would also support our work on DeepLabCut - a popular animal pose estimation system https
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experiments workflows in areas such as simulation, classification, or anomaly detection using machine learning and deep learning methods, exploring, in particular the performance of Quantum Computing
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libraries developed by CERN and its partners to deploy machine learning algorithms on FPGAs. Leading the development of a library for hardware-aware end-to-end training and optimization of neural networks
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motivated Engineer with a training in machine learning techniques to join our Efficient Particle Accelerators project team. In this role, you'll join the Accelerator Systems Department (SY) , more
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analysis. Within the NGT project, we seek to make best use of Machine Learning (ML) algorithms in this upgrade to exploit the full potential of the upgraded experiment. Responsibilities Develop ML algorithms
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interesting collisions for further analysis. Within the NGT project, we seek to make best use of Machine Learning (ML) algorithms in this upgrade to exploit the full potential of the upgraded experiment
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analysis, processing, machine learning and statistical interpretation of HEP data at CERN in the ROOT project, taking advantage of hardware accelerators. Join CERN's SFT group in the Experimental Physics
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: PhD degree in Computer Science, Mathematics, or similar fields Advanced knowledge of machine learning concepts Ability to present to technical and non-technical audiences Open to
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of an integrated framework. Proficient usage of software development tools (e.g. Github, GitLab) and continuous integration. Basic knowledge of Machine Learning and Machine Learning Operations would be a plus