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project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment This is a 2–year position from 1 October 2025 or as soon possible. Job description You will be
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mechanisms that dynamically alter the detrapping and recombination probabilities. The aim of this project is to overcome these limitations by developing a Monte Carlo simulation model that treats each charge
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Job Description We invite applications from highly motivated and talented researchers for a 2-year postdoctoral position in advanced skin models, cellular immunology, and spatial transcriptomics
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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research, e.g., sparse data analysis, generative models, batch correction and multi-sample/-modal data integration, etc. Experience extending and fine-tuning DNN models for novel tasks Experience with a
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Foundation Classes (IFC), and linked data Sensors as part of Internet of Things (IoT) and integration of sensory information in simulation models during run-time Data processing, incl. artificial intelligence
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, Hvidovre hospital; Ronn-Larsen, KU) whose combined expertise in mucosal immunology, gastroenterology, microbiota-host interactions, bioinformatics and state-of-the-art animal models aims to identify
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on quantitative methods such as applied simulation, big data, artificial intelligence, and machine learning as well as an ability to apply such skills to interventions research, field experiments, and
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researchers and students. Many promising methods based on generative AI have been suggested to generate synthetic data that can be safely shared in support of exploratory analysis, model prototyping, and IT
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Applications are invited for a 1-year post-doc position on modeling of two-phase flows for green hydrogen application at the Department of Mechanical and Production Engineering, Aarhus University