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project The Chair of Numerical Modelling and Simulation at EPFL is dedicated to the design and analysis of numerical algorithms for partial differential equations. Our research is oriented towards
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measurements; Modeling of indoor pollutant emissions, transport, transformation, and exposure, ranging from mechanistic or mass-balance approaches to more advanced computational models; and Related topics in
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, environmental modelling and geospatial data science. The position is part of Vertical Africa (VERTICAF), an interdisciplinary research project funded by the Swiss National Science Foundation (SNSF). We
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. Scalable AI training: distributed training, efficient fine-tuning, evaluation, and deployment on large-scale compute. Multimodal learning: representation learning and generative modeling across heterogeneous
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advanced computational models; and Related topics in indoor air quality, aerosol science, exposure science, and healthy buildings. Projects may combine controlled laboratory experiments, measurements in real
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advanced statistical and computational analyses, including trial-level models, mixed-effects models, clustering or latent-profile analyses, dimensionality reduction, predictive modeling, cross-validation and
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of SATURNA, namely Language Models for RNA sequences, Graph representation learning and generative models, Knowledge-Augmented RNA modeling, Computational RNA biology and Experimental stem cell biology and RNA
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vision, video understanding, action recognition, multimodal/vision-language models, pose estimation, or large-scale self-supervised learning. Familiarity with neuroscience, or ethology is a plus. Position
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computational modeling, mathematical modeling, or experimental psychology. Profile We thrive on interdisciplinary collaboration. You should hold a PhD in musicology, computer science, cognitive science
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communities as a model system. Main duties and responsibilities Anaerobic digestion communities are among the best-characterized and most ecologically important microbial ecosystems. Composed of diverse