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models of regulation and dynamics . Flow- and diffusion-based models of cellular dynamics are expressive enough to map any source to any target state, but they fall short of learning the underlying
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laboratory work, good organizational skills and the ability to work independently. Applicants should enjoy learning new methods and working in a collaborative research environment. Enthusiasm for infection
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processing, machine learning, and human-computer interaction. The goal of this PhD is to develop neuromotor interfaces for dexterous robot teleoperation using two sensing modalities: 1) surface
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architectures that integrate diverse biological data modalities. Multimodal representation learning: aligning and fusing information across modalities to capture biological systems at multiple scales. Agentic
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60%-80%, Zurich, fixed-term The Strategy & Analytics Group in the Unit for Teaching and Learning provides the evidence behind decisions about teaching at ETH Zurich. The team runs ETH-wide projects
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methods involving molecular surface display and deep sequencing to study force-dependent behavior in protein systems. These datasets will, in turn, be used to train machine learning models capable
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of the position will conduct research and teach on the relationship between inequality and politics, broadly defined. Substantive areas may include, but are not limited to, the drivers of inequality, its impact on
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development of metacognitive skills across childhood and adolescence. Metacognitive skills play a central role in successful learning because they enable learners to monitor their own understanding, recognize
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practical web development. Some technologies may be learned during the project, but solid Python skills and hands-on experience with graphs and GNNs are essential. Full-stack: able to own backend, data layer
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dynamics (MD) simulations including enhanced sampling techniques as well as machine learning. Profile Applicants should hold a M.Sc. in computational chemistry, chemistry, biochemistry, or physics