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The Department of Mathematics The Department of Mathematics strives for excellence in research. The department balances pure mathematical research with mathematical research motivated by applications. Researchers in the department are on one hand active at a fundamental and theoretical level,...
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-inspired computing paradigm with the potential to drastically reduce energy consumption while enabling faster inference than conventional digital architectures. A major challenge, however, is the development
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beam (FIB) imaging - can be combined with AI to reconstruct nanoscale chip structures and infer functional behaviour from physical layouts. The project addresses the challenge of extracting reliable
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structures with extraordinary precision and do so quickly enough to keep up with large-scale production. This creates a fascinating computational challenge: how can we infer hidden physical properties from
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statistical inference/AI to unravel how cell–cell interactions shape collective cell migration. A central goal of the project is to develop and apply data-driven theoretical frameworks that infer interaction
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environment. Who are looking for candidates interested in combining non-equilibrium statistical physics, active matter physics, and statistical inference/AI to unravel how cell–cell interactions shape
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Infrastructure? No Offer Description Waves are widely used for imaging in applications ranging from geophysics and medical ultrasound to non-destructive testing. In these applications, the aim is to infer
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Health Data Sciences and Informatics (OHDSI, https://ohdsi-europe.org ) initiative, dedicated to bring out the full value of observational health data through the OMOP Common Data Model and large-scale
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for tail modeling and statistical inference on rare events that lie outside the range of the available data. We foresee applications of the developed methodology in several fields, but within this project we
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modelling, ecological forecasting, time-series analysis, Bayesian statistics, and statistical programming (primarily in R and Stan). You will gain experience working with large, long-term, multidimensional