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Field
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University Tandon School of Engineering The Dynamical Systems Laboratory at NYU Tandon School of Engineering is seeking to hire a Post Doctoral Associate to work on the mechanics of deep-sea sponges. We seek
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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data (PET, CT, Magnetic Resonance Imaging with Late Gadolinium Enhancement – MRI-LGE) and clinical variables. The approach encompasses unsupervised multimodal registration, three-dimensional deep
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. This position offers an exciting opportunity to work in a dynamic, innovative research environment. Labor Contract: https://ucnet.universityofcalifornia.edu/labor/bargaining-units/px/index.html Lab: https
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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning
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environment to study these topics given its expertise in Machine and Deep Learning, Computer Vision, Signal Processing, and Multimedia. Also, its declared vision to work especially in presence of imperfect data
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Classification Title: Post Doctoral Associate Job Description: The Department of Epidemiology (http://epidemiology.phhp.ufl.edu/) is strategically situated within the College of Public Health and
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 2 months ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning