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year, with a prospect of becoming indefinite) on the development of the data-analysis suite and pipeline infrastructure for the upcoming gravitational-wave space observatory LISA. In 2015, the LIGO–Virgo
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Your profile We seek candidates with an outstanding research record in deep learning, in particular in one or several of the following areas: modeling and architecture development, domain adaptation
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interdisciplinary study programmes. Candidates with background in biology, bioengineering, biotechnology or microbiology with experiences in method and technology development are welcome to apply as well
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or microbiology, with a strong interest in technology development, are welcome to apply as well. The combination of biological and technological aspects is central in our group and in this project. A possible
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interdisciplinary project on the development of new computational approaches for improving safety and sustainability of (nano)materials based on publicly available data and tools, including omics data, and
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. We are working closely together with ETH researchers to provide support in the wide area of scientific computing from data management and analysis, development of scientific software, to porting and
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development of algorithms and large-scale numerical simulations. Your expertise will extend to various areas, including quantum Monte Carlo, machine learning, quantum computing, quantum machine learning, and
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quality journals Personal and professional development support You will be based at Empa in St. Gallen (Claudia Som, principal supervisor, Dr. Roland Hischier and Prof. Dr. Bernd Nowack, co-supervisors) and
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. 10007007; project start 01.10.2025: collaboration partners: J. Löffler, ETH Zürich; K. Klein, Universität Zürich; R. Müller, ETH Zürich, B. Schaller, Universitätsspital Bern) focusing on the development
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independent analytical work with active participation in joint project planning, interpretation, and dissemination of results. Your responsibilities will also include the development and maintenance