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creation of decarbonized, resilient, and equitable energy systems. This PostDoc position is offered in collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL ( Prof
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we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and
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on artificial intelligence and causal inference for the social sciences, in close collaboration with the Data Science and AI division at Chalmers. The employment This employment is a temporary contract of two
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, an interdisciplinary effort at the interface of genomics, artificial intelligence, and biophysics. The project aims to elucidate the mechanisms governing DNA replication initiation in metazoans by combining deep
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tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research
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October 2026 Apply now Do you want to use artificial intelligence, geospatial data science, and urban analytics to create healthier and more equitable cities? Are you excited about developing innovative AI
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of artificial intelligence in psychotherapy. You plan and conduct your own scientific studies. You publish articles in international peer-reviewed journals. You give presentations at national and international
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focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in
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candidates will be working with a team of researchers on a range of topics, including artificial intelligence, RF sensing and communications, multimodal UWB RF signal data analysis, and wireless sensing
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scientists. You meet the following criteria: • a PhD, or a PhD close to completion, in machine learning, artificial intelligence, computational biology, bioinformatics, computer science or a closely related