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using methods such as surveys, qualitative interviews, focus groups, or computational social science approaches Drive innovative research and policy projects on the ethics and governance of digital health
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range of topics related to imaging with geophysical data. Our research focuses on mathematical methods for processing, imaging, and inversion of geophysical data, the physics of wave propagation, and the
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policy, political economy, economics, sociology, computational social sciences, or a related field Strong knowledge of advanced quantitative methods is essential (e.g., econometrics, causal inference
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energy transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal
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Foundation (SNSF) funded Ambizione grant, starting between May 1st and September 1st. Applications submitted before March 15 will be considered. This role focuses on idealized, numerical modeling of severe
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transdisciplinary knowledge. Job description We offer a full doctoral student position in our research group. The doctorate will be conducted within the Einstein School’s doctoral program and is expected to be
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of mathematics, computer science, and evolutionary biology. We develop methods to understand evolutionary, ecological, epidemiological, and developmental processes on different scales based on genetic data. In our
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Pharma Research and Early Development (pRED). The program focuses on developing new tools and methods to address challenges in medicine and therapeutic development. Project background Adeno-associated
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Swiss Society (CERESS) will connect with numerous further universities and climate centers across Switzerland. CERESS will support nationwide research, co-design living labs, and produce actionable
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postdoctoral researcher in Robot Learning who is interested in questions such as: How can we fuse representations of the world with modern learning-based methods to allow for larger scale / longer horizon? How