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Field
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diverse non-model organisms, such as Asgard archaea and microbial eukaryotes, using a combination of cryoET, cryoEM and complementary imaging techniques. For examples of our work, see https
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content imaging, multi electrode array technology or reprogramming of somatic cells into pluripotency is necessary. Experience in computational image analysis and aging research is a strong plus. A
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computational research. In particular: • A high-quality imaging platform • A dedicated biocomputing hub that guarantees reliable data storage, management, and advanced analytical capacity. Our laboratory is
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/id_offre/133212 Requirements Specific Requirements Le/la candidat(e) devra posséder : — Une solide formation en mathématiques appliquées, incluant le traitement du signal et/ou des images. — Une bonne
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. This position offers a unique opportunity to contribute to cutting-edge research in medical imaging while actively engaging in teaching and mentoring students and trainees. The ideal candidate will possess a
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
of approximately 300 scans is planned to allow the model to be trained. Access to this imaging has already been anticipated through the Health Data Warehouse (AAP Entrepôt de Données de Santé EDS 2023) via
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linking in a multimodal framework, seeking to jointly exploit the visual and textual modalities present in texts accompanied by images and knowledge bases also enriched with images, in order to facilitate
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communication skills and a collaborative mindset Full professional proficiency in English Good to have: Hands on experience with at least one paradigm for quantum optimization with quantum computing (QAOA
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, including basic processing of photoemission data, as well as more advanced processing for HAXPES data or photoemission imaging. *Participate in measurements at large-scale facilities such as synchrotrons
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simulation tools and machine learning techniques. Initially, the work will be based on the design of a database of medical images, which will be processed using convolutional neural networks to identify design