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reduction and uncertainty quantification for biological flows. The goal of the project is, in particular, the development of robust methods to quantify the propagation of domain uncertainties in
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methods to quantify the propagation of domain uncertainties in cardiovascular simulations, especially with regard to the estimation of biomarkers for image-based diagnostics. The project is a collaboration
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responsibilities: PIK is seeking a qualified junior researcher or postdoctoral scientist responsible for the estimation of future fire risk with the use of the updated SPITFIRE model, based on future projections of
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is seeking a qualified junior researcher or postdoctoral scientist responsible for the estimation of future fire risk with the use of the updated SPITFIRE model, based on future projections of
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bases and instruments in terms of their efficiency and incidence Applied game-theoretic modelling based on 1) and on numerical estimates of the benefits that potential donor countries derive from
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methods to estimate the uncertainty in the daily predictions What do we expect from you? We seek a scientifically curious researcher who is passionate about understanding the environment. You should hold a
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and apply cutting-edge methods in artificial intelligence (AI) to address urgent societal and environmental challenges such as improving food security, promoting animal welfare, protecting biodiversity
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or related fields Expertise in advanced neurofunctional assessments and methods (e.g., computer vision, bioacoustics, machine learning/AI, neuroimaging, eye-tracking, multimodal data fusion) Excellent
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the team of Prof. Dr. Loriana Pelizzon on data and methods for supervision of climate and ESG risks in Capital Markets (incl. investment funds, bonds, stocks). The role involves advising and
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage