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Field
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of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
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Theoretical Quantum Materials We invite applications for a PhD position in theory of quantum materials at Aalto University, Department of Applied Physics. The position is part of the FUN-VAN Research Council
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Exciting Opportunity: Join UCC’s Cork University Business School (CUBS) to shape the future of business theory and practice - Several PhD and MSc (by Research) studentships available for October
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
interfaces. Topics of interest include: Planar and geometric graph algorithms Approximation and parameterized algorithms Clustering, embeddings, and structural graph theory Computational complexity and
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theory. While the GW method reliably describes photoelectric (main) peaks, it often fails to accurately capture satellite positions, which require more advanced techniques such as the cumulant approach (GW
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theory to join the cross-cutting theme entitled ‘Co-creating Concepts of Care.’ The cross-cutting theme focusses on co-producing innovative understandings of racialised inequality in care to understand
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computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably
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powerful ideas and tools at the intersection of topological band theory, symmetry analysis, and photonics. You will work on developing and applying these ideas to discover new topological phenomena, design
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will