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documents involving both text and images for the FED-tWIN programme - Prf-2022-025 - ARKEY #AI #NLP #ComputerVision #HTR #MetadataExtraction #UX #InfoVis Context FED-tWIN is a research program of the Belgian
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of movement or imaging data in humans. Experience with biophysical modeling of deep brain stimulation is preferred, but not a requirement. The research position, in conjunction with the University of Minnesota
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visual dimensions in the context of face, animacy, and object representations more generally. We collect and analyse human behavioural and brain imaging (fMRI and M/EEG) data. We also analyse macaque
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assays, and immunofluorescence imaging. Assists in the establishment of performance standards, the selection of research methodologies and instrumentation, and coordination of experimental approaches in
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advanced imaging to dissect the cellular signaling and regulatory networks that control photosymbiosis between cnidarian hosts and their microalgal symbionts. The Project - PhagoPhoRe This position is
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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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and tracking in LEO constellations, and monostatic and bistatic SAR imaging using communication waveforms Design and evaluate self-interference cancellation (analog/digital) and synchronization
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(typically a few trips per year.) BNL policy requires that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience
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) can be used safely to image and regulate mood state in a closed loop fashion in healthy volunteers as well as patients with prior history of brain damage, using state-of-the-art neuroimaging methods
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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with