14 machine-learning-"https:"-"https:"-"https:"-"https:" positions at University of Basel
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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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PhD in one of these areas. Candidates should have an exceptional academic record, a strong background in machine learning, and a robust mathematical foundation. They are also expected to have strong
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schools, where children and adolescent research participants will complete computer-based learning tasks designed to capture metacognitive monitoring and regulation during task performance. The successful
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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Building a quantum computer that can solve real-world problems will require the integration of many highly coherent qubits. Hole-spin qubits in planar germanium quantum wells are among the most
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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, and analysis of large and heterogeneous data. Within the DMI, the new professor will build bridges between distributed systems, data science, and machine intelligence through shared interests in
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environment for doctoral researchers interested in longitudinal and mixed-methods approaches to language learning and mobility.
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to exchange ideas with other doctoral students, receive feedback on the work in progress, and acquire the skills necessary for an academic career. The Doctoral Program also supports its members with