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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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methods from both Mathematics and Physics, complemented by concepts from Theoretical Computer Science. The group is located jointly at the Faculty of Physics – as part of the Quantum Optics, Quantum
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data, stochastic optimization, modern machine learning methods, scalable algorithms for advanced Machine Learning techniques and explainable AI. In teaching, the position will contribute, inter alia
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questions in the field of mathematical automation in numerical analysis or approximation theory. o You implement the developed methods in Lean. o You become an active team member of the "Mathematical Data
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Assistant Praedoc, you will pursue a PhD under the supervision of Janina Kehr at the Institute of Cultural and Social Anthropology with a topic of your own choice and join the Health Matters research group
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. Visual Data Analysis you will develop methods/tools to enable more effective research, e.g., enabling medical professionals to explore and discover patterns in disease progression and to assess treatment
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both older and more recent literary history. Ideally, they are experienced in interdisciplinary co-operation and have an interest in the methods and theories of literary research. They are also
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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 with IMP, GMI
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astrophysics, and uses 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
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step toward obtaining the formal qualifications needed (particularly the habilitation), and as a chance to develop a distinct, visible research profile within the academic community. Completed domestic