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Scientist / Scientific assistant – Tree species detection using deep-learning Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and
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to develop a novel instrument for near real-time detection of airborne pathogens. The position is part of a collaborative and interdisciplinary project involving the Swiss Tropical and Public Health
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to develop a novel instrument for near real-time detection of airborne pathogens. The position is part of a collaborative and interdisciplinary project involving the Swiss Tropical and Public Health
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a novel instrument for near real-time detection of airborne pathogens. The position is part of a collaborative and interdisciplinary project involving the Swiss Tropical and Public Health Institute
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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; Tanna, Nature Protocols, 2020) that encodes transient cellular events into DNA and reads them out by sequencing. Computational challenges include detection of biological signals while applying Record-seq
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between Gramazio Kohler Research (Chair for Architecture and Digital Fabrication) at ETH Zurich and the Chair for Timber Structures (Prof. Dr. Andrea Frangi). Job description The objective of this PhD
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) that encodes transient cellular events into DNA and reads them out by sequencing. Computational challenges include detection of biological signals while applying Record-seq to complex in vivo environments
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Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral
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the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and