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, a novel spatial discovery proteomics concept that integrates microscopic cell phenotyping with deep-learning based image analysis and global MS-based proteomics. This unique method was recently
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programming and instrument control using Matlab, Python, Labview etc Machine / deep learning expertise Strong analytical skills and ability to work in a multidisciplinary team Excellent communication and
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discipline Strong experience in integrating several of the following components: Deep learning and LLMs for molecular biology Vision foundation models for pathological image analysis Multi-omics datasets (e.g
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techniques Background in biomineralization or structural biology, or desire to work within research that has a marine environmental sustainability perspective Previous work with deep learning frameworks (e.g
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We invite applications for a 24-month postdoctoral scientist position to join our team within the project ReFuel: Harnessing archaeal processes to capture carbon dioxide into alkanes as renewable
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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spectral imaging, lifetime data, or multi-channel image datasets. Solid background in chemometrics, machine learning, or deep learning, particularly for classification, clustering, or pattern recognition in
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venuesStrong programming skillsSolid mathematical foundation, including linear algebra, probability, statistics, and optimizationBroad and in-depth experience with machine learning algorithms and deep learning
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
hybrid models that integrate limnological knowledge into machine learning models following the paradigm of Knowledge-Guided Machine Learning (KGML). The position is part of an on-going project