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& ecosystem structure under rising biosphere novelty. The work will integrate ecological, demographic, environmental and remote-sensing information to develop scenario-based forecasts of European vegetation and
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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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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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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
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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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QTLomics as part of the project. Main responsibilities Collect and standardise functional information, including QTL data, RNA-seq, and Gene Ontology (GO) annotations Develop computational pipelines for QTL
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Quantitative proteomics workflows Development of LC-MS/MS methodologies Post-translational modification analysis Data-independent acquisition (DIA) approaches Large-scale proteomics studies Laboratory automation