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Position Description The Unsteady Flow Diagnostics Laboratory (UNFoLD) led by Prof. Karen Mulleners at EPFL in Lausanne is looking for multiple PhD students to join the group in the fall of 2025 or early
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Knowledge, Skills, and Abilities PhD in Neuroscience is required. Experience with in vivo imaging in head-fixed behaving mice is required. Experience with Arduino microcontrollers for the design and control
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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hospital. About The Role Magnetic resonance imaging (MRI) is an indispensable clinical tool, but highly sensitive to even small movements. For head MRI indications, multiple scans are needed, each requiring
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complete picture of fish habitat use and connectivity. The PhD is part of the section for Ecosystem based Marine Management and the Marine Habitats research group, as well as several synergistic initiatives
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mixture of independence and collaboration with the Associate Dean Research and other laboratory managers reporting to this office. The manager will also process and image samples, as needed, until
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aberration-corrected scanning transmission electron microscopy (STEM), high-resolution TEM (HRTEM), energy-dispersive X-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), and advanced image
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square foot Tower are several state-of-the-art treatment technologies, including: three Varian TrueBeam linear accelerators with optical surface imaging; respiratory gating and a 6DoF couch; an M6
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. Preferred Qualifications Expertise in computational data analysis and familiarity with software platforms for image and electrophysiological data processing. Knowledge of bioinformatics, AI-driven phenotyping
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transcriptomics, highly multiplexed imaging, and histopathology. We integrate these data with clinical and molecular information to investigate the organization of cells at the micro-anatomical level and how