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computing. This will include, but is not limited to, the design of distributed quantum algorithms, circuits, and error correction, as well as the interplay between circuit optimization and circuit
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and pattern recognition, atmospheric modelling, and computational spectroscopy. We focus on in-depth understanding of data and related algorithms, data analysis, and machine learning. Our cross-cutting
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the development of innovative mathematical and computational algorithms. As our new Postdoctoral Researcher, your main responsibilities will include: developing and implementing advanced mathematical and
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-depth understanding of data and related algorithms, data analysis, and machine learning. Our cross-cutting theme is machine learning-enhanced computational engineering. You’ll have an excellent
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, and intelligent decision-making across distributed manufacturing networks. Specifically, the work will include: Development of a digital twin for a manufacturing production system. Development
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(land, aerial, marine, or hybrid) Distributed decision-making, task allocation, and worksite optimization under uncertainty Energy-aware and resource-efficient collaboration strategies for fleets
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identification algorithms that directly interface with physical hardware. We work closely with industry partners, and our research has led to several methods now used in commercial products. We are part of
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collaborators. Your work will develop algorithms, inference methods, and frameworks to adapt models from training data to test environments, which is necessary to resolve distribution shifts, hidden confounders
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diversity (e.g., composition,size- and spatial distribution of trees) and diversity of other species in the forest are linked. The focus will be on understanding the spatial scales of these relationships and
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to either: Extend state-of-the-art joint species distribution models (JSDM) to analyze ecosystem functions provided by species communities. JSDMs are multivariate hierarchical Bayesian models, with parametric