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learning in context. Our research examines mechanisms of human learning, the conceptual structure of human knowledge, how people communicate expertise, and effective strategies when trying to build new
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of cardiovascular specialty and subspecialty areas. The UWMC CV APP Fellowship is an intense, highly structured, comprehensive program through which newly licensed APPs will acquire in depth cardiovascular knowledge
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following use cases: • The construction of a machine learning pipeline that allows the conversion of Course Unit Sheets (CUS) into a data structure based on the European Learning Model (ELM). • Integration
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experiment is a next-generation long-baseline neutrino oscillation experiment that is currently under construction in Japan. Hyper-Kamiokande will use an upgraded (anti)neutrino beam and a water Cherenkov
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-specific T cell isolation, expansion, functional characterization, and cloning Learning T cell receptor (TCR) sequencing and recombinant expression for mechanistic and structural studies Applying HLA
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Austrian Academy of Sciences, The Institute for Quantum Optics and Quantum Information Innsbruck IQOQIIBK | Austria | about 1 month ago
of MyAbility in order to provide appropriate workplace structures, in particular for persons with disabilities. Where to apply Website https://oeawnr.onlyfy.jobs/application/apply
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photophysics under conditions simulating those of the interstellar medium. Structural characterization, electronic absorption, electronic fluorescence, and recurrent fluorescence are studied through in situ
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employment conditions. Successful candidates become members of the national DDLS Research School, which offers a structured training environment, national networking, courses, seminars, and scientific visits
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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | 8 days ago
approaches. The program is structured as a transatlantic exchange between research environments in Germany and the United States. Fellows are expected to conduct their research outside of the country in which
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machine learning (ML) methods are widely used to explore structure in complex and high-dimensional data, particularly in the life sciences, where clustering analyses often form the basis for biological