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postdoctoral position is available in the laboratory of Prof. Alex Persat (EPFL School of Life Sciences, https://www.p-lab.science/ ) to establish a new image-based drug screening platform targeting gram
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indoor air quality, aerosol science, exposure science, and healthy buildings. Projects may combine controlled laboratory experiments, measurements in real-world buildings, data analysis, and modeling
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of an Anosov representation. For instance, the boundary at infinity of a hyperbolic group naturally embeds as a subset of a flag manifold for an Anosov representation. The AnSur project aims to study the global
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protocols. In close collaboration with the lab of George Coukos (https://agora-cancer.ch/laboratory/coukos-lab/ ), we will derive protocols enabling rapid expansion of cancer-specific, autologous TILs
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advanced computational models; and Related topics in indoor air quality, aerosol science, exposure science, and healthy buildings. Projects may combine controlled laboratory experiments, measurements in real
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of Competence in Research NCCR Genesis is a research infrastructure project funded by SNSF focussing on exploring the emergence of life in the Universe. This interdisciplinary research infrastructure brings
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoc in ML-Based Co-Design at EPFL Mission The EPFL computer vision lab (https://www.epfl.ch
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on ongoing projects. We welcome young scholars in entrepreneurship, strategy, and technology and innovation management, using quantitative, qualitative or experimental methods. The Chair's research appears in
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motivated Postdoctoral researcher (W/M) to join our Neuroengineering Laboratory at EPFL (https://go.epfl.ch/ramdya/ ) to work on an ERC funded project aiming to reverse-engineer insect limb motor control
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responsibilities The Kim Laboratory at the École Polytechnique Fédérale de Lausanne (EPFL) (https://www.epfl.ch/labs/upkim/ ) is seeking a highly motivated postdoctoral researcher to investigate how neuronal