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Field
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research and implementation. You will also develop novel algorithms using state-of-the-art computer vision and machine learning techniques (segmentation, multimodal AI, foundation models, agentic frameworks
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cognitive flexibility tasks (eg, reversal learning, set-shifting), including participant recruitment, data acquisition, and quality control. Process and analyse high-density EEG data, including artefact
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to, flow cytometry, ELISA, RT-PCR, bulk and single cell RNA sequencing, imaging and other emerging technologies (e.g. imaging mass cytometry, spatial transcriptomics, metabolomics/proteomics) – including
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are invigorated by the creative potential of our global collections to impact teaching, learning, and research across Cornell University, and to broaden our reach as a world-class academic art museum. The Johnson
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include: Biomedical sensing and physiological monitoring Edge intelligence and energy-efficient machine learning hardware Radar and wireless signal processing and communications The successful candidate
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! Be
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, scientific, and regulatory steps required to translate in vitro molecular hits into small animal preclinical evaluation models. Processing and integrating complex transcriptomic, proteomic, and image-based
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Post Doctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI
techniques. This strand would involve adapting in-house models for Positron Emission Tomography data to other imaging modalities. Research in the other strands that focus on machine learning applications will
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
simulation, shape/topology and system-level design optimization applied to deformable systems — advanced level Data-driven design and modelling approaches (machine learning applied to physical/mechanical
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biofluid samples Conceive, prototype, and benchmark new computational and AI methods for liquid biopsy, including techniques to bridge to other data modalities, e.g. imaging Apply modern machine learning