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who bring deep domain expertise in one or more of the following areas: Stem cells, organoids, tissue engineering, and/or 3D bioprinting Biomaterials for regenerative medicine and mechanobiology CRISPR
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experience in machine and/or deep learning applied to geospatial data. Demonstrated experience in the use of HPC and handling of very large datasets. Experience in software development and contribution
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knowledge of Efficient Learning for computer vision Coding Skills: Familiar with any of the major deep learning libraries, including Pytorch We regret to inform that only shortlisted candidates will be
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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within a Research Infrastructure? No Offer Description Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the
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control using deep learning. Implement and test new algorithms in actual robot platforms. Job Requirements: PhD in Electrical and Electronic Engineering or related field. Hands on research experiences in
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 19 days ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5
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a Research Infrastructure? No Offer Description The Department of Ecology, Environment and Plant Sciences (DEEP) is located in the Arrhenius Laboratories, situated at the University Campus at Frescati
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an advanced AI-augmented digital platform (AiCT-Med) powered by cutting edge machine learning models trained on multiple large, aged care datasets from providers across Australia. The platform is designed