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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against
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of Singapore, and EPFL (Switzerland). These partners are looking for talents in several domains of machine learning, AI, computational biology, and biology, to develop PhD theses across the main pillars
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of sensorimotor processing. We are recruiting two postdocs: Postdoc in Computer Vision & AI for Behavior Analysis Postdoc in Embodied AI (Reinforcement Learning for Motor Control) Main duties and responsibilities
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Finance at EPFL seeks postdocs to reinforce our research in quantitative finance with a focus on applied machine learning. Main duties and responsibilities Working and collaborating on research projects
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and
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of the upgraded Swiss Light Source synchrotron, as well as other facilities around the world. Profile We are looking for a person with: PhD in physics, engineering, computer science, or a related field. Strong
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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to the technical and quantitative training of junior lab members. Candidate profile Applicants should have a PhD in biomedical engineering, electrical engineering, computer science, data science, computational
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datasets with multi-omics profiles of tumors from previous studies Developing new machine learning models through collaboration with the Swiss Data Science Centre Establishing data analysis pipelines