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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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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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on the successful award of the PhD degree. Who we are CLINDA is a multidisciplinary center dedicated to bridging clinical practice and data science. It comprises three research groups: (1) Clinical AI, (2
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local and regional trends. The necessary samples and data have been collected in recent years during expeditions to the area, and include numerous sediment cores, acoustic surveys of the seafloor and sub
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and different industrial outreach activities The candidate has at least the following qualifications - Applicants should hold a PhD in Computer Engineering, Computer Science, or similar - Cyber-physical
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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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) 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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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