12 programming-"https:"-"https:"-"https:"-"https:"-"IDAEA-CSIC" research jobs at EPFL in Switzerland
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(or equivalent) in neuroscience, neuroengineering, computation, or a closely related field, such as EPFL's Neuro-X Master's programme. Excellent academic results and relevant coursework in translational
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-term sustainability and the local appropriation of the DOUV. Integrate the data produced by WP1, WP2 and WP3 into the tool; programme the automatic ingestion of Sentinel-2 imagery and the recalibration
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with a focused on micro-technologies. To strengthen our research program on laser-based methods to tailor material properties, we are looking for a post-doc to investigate advanced wavefront shaping
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part of a collaborative team to plan and execute experiments Maintain knowledge of published developments in research field and recommend alternative strategies Plan and organize workload to meet project
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Modelling (BIM), Geographic Information Systems (GIS) or computer programming is a plus. A master's degree or professional experience in architecture is preferred but not required. The employment rate varies
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. Proficiency in programming and statistics/data science. A genuine passion for music and a solid understanding of music. Fluent English and a collaborative team spirit. We offer World-Class Research: EPFL is a
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, engineering, computer science, or a related field. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related modality. Strong scientific programming in
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scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related
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, engineering, computer science, or a related field. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related modality. Strong scientific programming in
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, normalizing flows, VAEs, generative transformers, or related methods. Strong programming skills and experience with modern deep learning frameworks. Experience with large-scale model training, distributed