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programming in Python, R, or similar languages Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and
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, machine-learning methods and predictive calculations of defects and transport properties. The position will also be integrated into the wider CENSEMAT research environment at Aarhus University, allowing
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computational pipelines for QTL data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web
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reactor. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the
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data visualization, with a track record of designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into
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machine learning methods and pipelines. Qualifications Ideally, you hold a PhD in Data Sciences and have several years of experience collaborating with scientists in natural sciences, particularly
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-Intensive Systems group studies management and analysis of complex data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining
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as high-memory nodes and state-of-the-art GPUs. An interdisciplinary interdisciplinary environment, in which the candidate will be able to exchange across research fields (machine learning
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-time data into WRF-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus