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immobilized on microarray slides for antibody epitope mapping and for studies binding interactions with immune regulatory lectins (carbohydrate binding proteins) that are involved in cancer progression
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physical AI for smart urban management, with a focus on human–robot interaction in real-world urban environments. The postdoctoral researcher will support the design, deployment, and evaluation of physical
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the mechanisms of constructive remodeling of tissue engineered constructs by delineating how host tissue microenvironments interact, populate, and integrate into scaffold environments in vivo. Our group synergizes
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, interdisciplinary, interactive and dynamic team. We are generously core funded, use the newest state-of-the-art equipment and technologies. Our lab is strongly supported by our in-house core facilities, such as a
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at https://www.ars-grin.gov and https://grin-u.org/ . Benefits: WSU offers a comprehensive benefits package which includes paid sick and vacation leave; paid holidays; medical, dental, life and disability
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. The Postdoctoral Associate will give presentations of their work, will help write papers reporting on research results, will help supervise graduate students, and will closely interact with the PI and colleagues
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available). Public transportation costs are partially covered. The successful candidate will have access to the CROUS cafeteria. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR7104-DOMBRO-018
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University Hospital Essen, Collaborative Research Centre 1752 | Essen, Nordrhein Westfalen | Germany | 2 months ago
single-cell multi-omics Artificial intelligence & bioinformatics Translational oncology Open positions available from October 2026 | Remuneration according to TV-L E13 17 PhD positions 2 Postdoctoral
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Infrastructure? No Offer Description Your Responsibilities The ERC-funded project aims to investigate interactions of various metallophilic chemolithotrophic microorganisms with complex mineral materials. We
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in