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We are seeking highly motivated and talented applicants for a 1-year postdoctoral position (with the possibility of 1-2 year extension) in the area of efficient foundation model inference. Join us at
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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks
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more sustainable lifestyles when it comes to more plant-rich diets. We use an innovative connection of behavioural and social experimentation and agent-based models to understand and model how policy
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this project intends to pursue using an integrate-and-fire neural network model known as LAMINART. A key aspect of the model is the ability to select/segment visual objects, which may explain how
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end on 28 February 2029, but with possibility for an extension. Job description • You will be contributing to further development of the WRF-Chem model to handle a large range of bioaerosols in
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analytically underexplored conditions. It relies on modern machine learning approaches to model fragmentation behavior, support spectral interpretation, and enable future advances in peptide identification
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spectrometry datasets and the development of new computational strategies for fragmentation analysis under analytically underexplored conditions. It relies on modern machine learning approaches to model
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integrating observational data, climate model output and regional ocean modelling. Develop and apply a high-resolution hydrodynamic CROCO model, linking model output to the FlexSem model for modelling
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drought early warning and monitoring system for large-scale river basins. The project will explore both data-driven and model-based approaches for drought predictions, paving the way for a continental high
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contributing to further development of the WRF-Chem model to handle a large range of bioaerosols in particular pollen and fungal spores. The geographical focus is on both the urban environment and