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, we found that GAA deficiency in PD resulted in engorged lysosomes which significantly disrupted AT1 and AT2 cellular architecture. Lysosomes are an essential component of autophagy which is important
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transformer architectures for multimodal fusion (RGB + thermal, intraoperative video, OR signals, EHR, surveys); develop temporal and cross-attention components for longitudinal risk trajectories. Build robust
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21 Feb 2026 Job Information Organisation/Company CNRS Department Institut de Chimie et Procédés pour l'Energie, l'Environnement et la Santé Research Field Chemistry Physics Technology Researcher
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the project RIBOTICS (RNA Origami Technology in Cell Systems) funded by an Advanced Grant from the European Research Council (ERC). The intended starting date is 01st September 2026 or as soon as possible
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the project RIBOTICS (RNA Origami Technology in Cell Systems) funded by an Advanced Grant from the European Research Council (ERC). The intended starting date is 01st September 2026 or as soon as possible
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professional, academic, and research organizations. Basic Qualifications: A PhD in computer science/engineering or relevant area with an education and a research track record in HPC/AI/edge systems and storage
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logistics architecture. Monitor technological and methodological innovations in the industry. Promote research and development findings through publications in high-impact Q1 scientific journals and indexed
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qualifications You will lead the computational and AI-driven aspects of the project. Your responsibilities will include: Designing and implementing state-of-the-art deep learning architectures for protein
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
biomedical imaging applications Your profile Master’s degree or PhD in Physics, Optical Engineering, Photonics, Quantum Engineering, Biomedical Engineering, or a closely related field, with a focus on quantum
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to microelectronic devices and low-power, three-dimensional, non-von Neumann computing architectures. You will utilize ORNL’s unique ultra-high-vacuum, glovebox, and ambient atomic force microscopy capabilities