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Field
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to those with: (a) strong background in quantitative methods, statistics, computer science, geospatial data analysis and modelling; (b) experience in AI and geospatial computer version; (c) advanced
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lab, you will play an important role in advancing the research agenda. Your responsibilities will include: · Perform research on computational modeling of EF-hand proteins · Perform method development
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demand across agricultural and urban land uses. The team will (1) develop and validate locally relevant methods to estimate consumptive use across mixed urban–agricultural landscapes, (2) estimate
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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network of projects that involve remote sensing of the cryosphere and related data science methods: the European Research Council (ERC) Synergy Grant SnowShifts with University of Oslo (Prof. A. Kääb), EPFL
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and the research team members to ensure all project deliverables are met. Lead the design, development, and experimental validation of high-frequency ultrasound imaging methods, with emphasis on high
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through their work. You should demonstrate: Interdisciplinary research expertise related to science studies and science policy Strong knowledge of quantitative research methods and their application in
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, computational modeling, computer vision, natural language processing, or related AI-based methods. Strong programming skills, preferably in Python. Experience working with complex, multimodal datasets and
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fields such as computer vision and natural language processing, graph-structured data remain a rich frontier for methodological innovation, with many fundamental challenges and exciting opportunities ahead
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& Responsibilities: Design and implement methods to maximize the expression and activity of integral membrane metalloenzymes and associated proteins. Carry out activity assays using a GC-MS. Measure metal