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. The primary objective is to develop computational methods, using deep learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted
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criteria Experience in writing documentation on the topic of Deep Learning Website for additional job details https://www.utcluj.ro/media/jobs/2025/DOC154_GAlboYR.pdf Work Location(s) Number of offers
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University of California San Francisco | San Francisco, California | United States | about 17 hours ago
for this position is $167,800 - $251,700 (Annual Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and
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CORE A*/A conference paper. We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s
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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied
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) Application and further development of deep learning methods for automated object recognition and classification in point clouds and 3D data Establishment of a data processing pipeline for the efficient
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microfluidics, nano-electronics, nano-biomaterials, big data, and deep learning. Applicants must hold an M.D., Ph.D., or equivalent degree and have extensive postdoctoral experience, along with a strong
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Scientist / Scientific assistant – Tree species detection using deep-learning Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and
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Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its graduates to be work- and future-ready professionals. Its mission is to maximise the potential
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been established. This position will focus on the further development of various, machine learning and deep learning models to study molecular mechanisms and cellular phenotypes caused by the etiology