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Job Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job This is a one-year full-time position. It is possible this position might be extended for a second
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Applications of Ultrasound and Microbubbles” (external link) (external link) within the Faculty of Science at Toronto Metropolitan University, Toronto, Canada. Our research program is developing an image
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and demonstrated experience in computer vision or analysis of pathology images
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Laboratory of Oceanography of Villefranche | Villefranche sur Mer, Provence Alpes Cote d Azur | France | 9 days ago
toxicity associated with dinoflagellate blooms through the exploration of a novel paradigm, emphasizing chemical interactions within the marine microbial sphere as the primary driver. The first objective of
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07.12.2022, Academic staff The institute for communications engineering offers a position for a doctoral or young postdoctoral candidate with the topic “Communication and coding in quantum networks
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R&D Projects, by the Code of Administrative Procedure (CPA), all in their current wording, and by other applicable national and community legislation. Procedure Reference: IT137-26-310 I - Legal
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of radiotherapy, including proton therapy. This project will both aim at improving delineation and dose calculation in radiotherapy while reducing imaging dose and patient burden. The project will include
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sequencing, imaging and other emerging technologies (e.g. imaging mass cytometry, spatial transcriptomics) – including aspects of informatics and advanced data analysis. You will be responsible
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to join our team. Tasks may include: Develop methods to analyze multimodal digital N-of-1 trials (patient reported outcomes, wearables, images, audio) linking causal inference and deep learning Develop