242 learning-"https:"-"https:"-"https:"-"https:"-"https:" "https:" positions in Switzerland
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30%, Zurich, fixed-term The Professorship for Learning Sciences and Higher Education at ETH Zurich is seeking a motivated student assistant (30%) starting in October 2026. In this role, you will
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characterizing individual nanoclusters • Engineering and purify protein nanopores with tailored sensitivity to size, charge, and etc. • Developing data analysis pipelines and machine learning approaches for signal
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Graduate Institute of International and Development Studies, Geneva; | Switzerland | about 5 hours ago
, International Law. The successful applicant will supervise MINT MA theses and department MA theses and PhD dissertations, and teach four postgraduate seminars per academic year, normally two in IHP and two
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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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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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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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at EPFL is seeking for talented and ambitious postdoctoral scientists with strong fabrication skills (or a big interest to acquire such skills) to work in the young and dynamic field of Integrated lithium
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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