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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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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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have collected large amounts of long read sequencing data (PacBio HiFi) to build genome assemblies and integrate them into pangenomes . This allowed us to investigate the distribution of structural
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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
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with additional biophysical features Apply the framework to morphogenetic problems based on imaging data Run large-scale simulations on ETH Zurich’s high-performance computing (HPC) infrastructure
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capabilities of the existing C++ codebase Applying the framework to morphogenetic problems using real imaging data Conducting large-scale simulations on ETH's HPC infrastructure Performing model calibration
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80%-100%, Zurich, fixed-term Our research group on Law, Economics and Data Science at the Center for Law & Economics is looking for a highly motivated Data Science Research Assistant to develop and
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large language models. Project background Language is a skill unique to humans among other animals. However, little is known about the neurophysiological processes that enable the construction of complex
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to engineer human cells and develop advanced gene and cell therapies targeting cancer, as well as metabolic, neurodegenerative, autoimmune and infectious diseases. For more information, please consult our