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addressing prototype and its integration into existing robotic structures developed at LIRMM (Montpellier – France); realization of a compact soft robotic prototype. - Control Development of sequential control
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projects integrating omics, clinical and medical imaging data, contributing to biomarker identification and the development of computational approaches for healthcare. Key responsibilities include analysis
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and techniques in archaeology. The project has aimed to develop a two-part research programme. The first part focuses on developing and applying methods for 3D digital modelling, ranging from
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microscopy datasets now capture millions of single-cell images across diverse perturbations, but differences in imaging protocols, marker panels, and cell types limit their integration and reuse. A key
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities
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work at the interface between computational spatial omics, single-cell mass spectrometry (MS)-based proteomics, multiplex imaging, and bioinformatics. We have recently co-developed Deep Visual Proteomics
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Essential: Proficiency in fluorescence microscopy techniques (confocal, wide-field, live-cell); experience with microscopy-microfluidic systems or single-cell analysis; strong background in image analysis
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ideal condition to support 3D cell growth via cell-microgel scaffold formation and additive manufacturing. This highly interdisciplinary position will cover material synthesis, microgel production via
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this paradigm by quantifying antibiotic action at single-cell resolution. The scientific aim Our laboratory has established a large-scale live-cell imaging platform that tracks hundreds of millions
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award to Dr Darren Fayne “Mapping the human pocketome - using pharmacophores to enable drug discovery applications”. This project will model the 3D structure of all human proteins, identify plausible