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high-pressure and high temperature experimental (laser-heated and externally-heated diamond anvil cell, large volume press, and piston-cylinder apparatus) methods, eager to contribute to cutting-edge
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and interpretation of large-scale datasets, in particular next generation sequencing (NGS) data. The team covers raw data processing, high-performance computing, statistical evaluation, interpretation
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the framework of a large National Competence Center of Research (NCCR) “Antiresist” (for more information, view ) involving clinicians, academic and industrial research partners across multiple disciplines, a
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to revolutionize multiple branches of science by solving problems that cannot be tackled by classical systems. While efficient and large-scale quantum computers are still far from being commercialized, quantum
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. Empa is a research institution of the ETH Domain. Stabilizing our climate requires to urgently phase out fossil fuels and clean up the atmosphere from excess CO2, which will induce a large demand for
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channels: presentations at international conferences and workshops and publications in leading academic journals in economics. Project background We have fully vectorized data of a large number of musical
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codebase used for training large generative neural network models. This role requires a strong background in machine learning, software development, and the ability to work collaboratively in a research
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mandate is to support academic groups and research, hospitals, industry, and the public sector at large, including cantonal and federal administrations. The center accompanies and supports their entire data
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these subjects) is a must. Exprience with large-scale computations and data handling is required. Some bakground in nonlinear dynamical systems is also expected. The candidate must have excellent communication
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automating the detection of individual tree species in forests using deep learning. Specifically, you will: Focus on the application of deep learning techniques in Python to process spatial and aerial data