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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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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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developing new techniques for analyzing security of open-source components and large-scale applications. Applicants are expected to have a strong background in Programming Languages and Software Engineering
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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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candidate is expected to work closely with Professor Rubina Raja, on several tasks, including: supporting large research/publication projects (including copyediting and indexing) carrying out data collection
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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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advanced methods for applying large-scale Danish data. You will work across interdisciplinary projects that investigate how genetic, familial, socioeconomic, and environmental factors shape mental health
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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
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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