222 information-security "https:" "https:" "https:" "https:" "https:" "University of Cambridge" positions at ETH Zurich
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across spatial and temporal scales and their response to global changes. GEML is a knowledge and technology Hub aimed at syncing the scientific community with rapidly evolving genome engineering methods
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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 flourish. Sustainability is a
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100%, Zurich, fixed-term The newly established Plant Disease Dynamics group at the ETH Zurich is looking to recruit a fungal genome biologist interested in the ways in which transposons shape
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are offering a PhD position and a postdoctoral position. Project background The project RadiantDx is a multidisciplinary, collaborative project that addresses the diagnosis of sepsis. Sepsis is a severe health
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income mobility and related outcomes across generations, focusing on differences by gender, career trajectories, and perceptions and beliefs about intergenerational mobility. The project is led by Dr
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100%, Basel, fixed-term The Computational Biology (CoBi) group, led by Prof. Dagmar Iber, develops data-driven, mechanistic models of biological systems using advanced imaging and computational
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, Biostatistics, Software Engineering, Systems Operations, and Screening & Lab Automation. Embedded in this multi-disciplinary environment, the Software Engineering group builds data-centric software products
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body of work that already expresses crucial inquiries in light, matter, perception, and physical phenomena "and" is truly experimental. Building on the artist researcher's standing, she/he/they would
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are offering a PhD position and a position for a postdoctoral fellow. Project background The project RadiantDx is a multidisciplinary, collaborative project that addresses the diagnosis of sepsis. Sepsis is a
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10%-30%, Zurich, fixed-term We are looking for motivated student research assistants to join our project “Adaptive Physics-Informed Neural Operators with Reduced-Order Modeling for Complex Dynamical