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to bridge the gap between regulatory aspirations and real-world constraints and outcomes, offering innovative approaches to compliance monitoring in an era where traditional methods are increasingly
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a variety of approaches, such as stochastic processes, kinetic theory, variational analysis, finite element methods, and data-driven techniques. The Vienna School of Mathematics doctoral program
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materials discovery and machine-learning-accelerated materials simulation. You lead and guide the development of novel computational materials discovery and molecular simulation methods. You take a key role
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pertaining to organizational design decisions relevant to firms operating in (technologically) innovative environments. We are agnostic to methods and pursue formally analytical, simulation-based, ethnographic
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instrumental analytical methods and, on the other hand, will deal with the computer-aided analysis and processing of large analytical-chemical data sets. Experience and knowledge of digital teaching and
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Kleitz at the University of Vienna mainly focusses on the development of nanoporous materials, their characterization, and their applications in numerous fields, such as catalysis, adsorption, bio- and
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and developmental biology, chromatin, RNA and chromosome biology, and synthetic/computational methods (including AI/ML) that will address complex biological questions from a mechanistic angle. Together
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to the faculty committee of the Doctoral School within one year after coming to Vienna (Faculty public presentation). You independently teach courses in the BA program (proseminar, case study), one course per
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structural manipulation using electron and ion irradiation combining experimental materials physics, atomic-resolution (scanning) transmission electron microscopy, single-atom spectroscopy, and computational
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team: This is an opportunity to work towards a PhD in physics and to conduct world-leading research and teaching in molecular simulation and computational materials discovery. Fuel cells, photovoltaic