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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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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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physically and functionally coupled across domains of life. The project will involve working with syntrophic deep-sea consortia, performing strictly anoxic physiological experiments, and developing electrode
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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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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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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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of operating underground with minimal human intervention. By joining this project, you will strengthen your scientific profile while gaining deep hands-on experience in mobile manipulation, contact-rich robotics
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serving (Ray/VLLM), quantization and sharding, prompt optimization, reinforcement learning, Transformers/Deep-SSMs/Test-Time Regression Extensive knowledge of agentic AI systems research, engineering and
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. Knowledge of Danish is not a requirement but will be considered positively. Qualifications The applicant must hold a PhD or equivalent qualification. Particular emphasis will be placed on research experience