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valued. · Knowledge of chemical reactions and how to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials
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the Texas Children's Cancer Center. The project aims to develop and test novel CAR-redirected immunotherapy for pediatric solid tumors. In particular, we want to design and test a regulated PRDM1 knockdown in
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probabilistic machine learning and geospatial sciences. Limited teaching may be arranged, if mutually agreed, in exchange for a contract extension. Qualification Requirements Applicants must hold a PhD degree in
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Machine Learning Formal
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of Position Temporary Position Time Status Full-Time Required Education PhD or PsyD Required Related Experience None Required License/Registration/Certification None Physical Requirements Standing or sitting up
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: What are efficient machine learning strategies to identify large ensembles of nanoparticles in tomograms (i.e., to identify nanoparticles on irregular 2D surfaces in 3D space)? What are appropriate
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multidisciplinary collaboration, scholarly advancement, and the creation of tools and public goods.RequirementsApplicants must have (or expect to have at time of appointment) a PhD in the social sciences, statistics
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Requirements REQUIRED: Ph.D. in Atmospheric science, Civil and Environmental engineering, mechanical engineering, or a related field; knowledge of and demonstrated skillset in physics-informed machine learning
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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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(postdoc) Limited until: permanent Reference no.: 4984 Among the many reasons to research and teach at the University of Vienna there is one in particular, which has convinced around 7,500 academic staff