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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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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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Are you interested in working with sensitive health data and making a real impact on how it can be used safely and responsibly? At the Center for Clinical Data Science (CLINDA), Department
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datasets High proficiency in data analysis and statistical programming using R and/or Python. Experience working in high-performance computing (HPC) environments for large-scale analyses and with workflow
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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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are software representations of physical assets, processes, or systems. They leverage real-time data to mirror the behaviour and characteristics of their physical counterparts, enabling predictive maintenance
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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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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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of collaborators in ecology, envi-ronmental genomics, pollinator biology and biodiversity monitoring. Key Responsibilities The post doc will: Design and coordinate large-scale field studies across multiple seasons