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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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) to study atomic structures of optically excited small unit cell crystals. The project involves measurements, data reduction and structure refinement of large serial femtosecond X-ray (SFX) crystallography
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identifying ecological and functional connections between environmental and host-associated microbiomes. The postdoctoral researcher will work closely with a PhD student and an international network
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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 particular pollen
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Computational Sciences or similar. You have strong expertise on analyses of biology-related large datasets. Expertise in single-cell and spatial data analysis, spatial statistics and annotation is an advantage
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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and proficiency working with large datasets (experience working with Danish national registers strongly preferred) Experience and proficiency with data analysis and statistical modeling Competence with
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, membranes, neuroscience and personalised medicine. The Department of Biomedicine provides research-based teaching of the highest quality and is responsible for a large part of the medical degree programme
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the principal SCS crystallographer. Key responsibilities will include outreach, SCXRD data collection, data processing, structure solution, and user support. To make SCS a success, the successful