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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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Boreal forest recovery after clear-cut: We are looking for a highly motivated PhD student to collect data on and model hydro(geo)logy and greenhouse gas exchanges of boreal forests after
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energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA
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, current models only predict the potential for events rather than actual specific landslide occurrence. These models also struggle to directly quantify landslide hazards and to address key characteristics
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computing. Current challenges in quantum technology adoption stem from the lack of standardized benchmarking methods and the inherent difficulty in validating quantum devices beyond classical simulation
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trends to provide immediate post-race feedback to Sport Directors that can be used to assess race strategy and tactics. Research, review and develop models based on objectives 1 and 2 to develop a race
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across
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of the assembly of these complex microbial communities using ecological theory and mathematical models. The questions we address are: (1) how does the microbial community change during cultivation
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bioinformatic skills to predict the evolution of rare diseases? FSHD is a rare neuromuscular disorder. No approved treatment is currently available. Slow and variable disease progression complicate trial design
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(ToF-PET) provides critical functional and molecular insights to improve cancer staging but is currently limited by detector timing resolution and sensitivity. Metascintillators, an emerging family of