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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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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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in using natural language processing (NLP) methods on large-scale text data. Candidates with substantial experience in NLP and text-based analysis will be particularly well suited to the project
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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
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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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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