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for architecting Northeastern University's transition from legacy data structures to a modern, scalable, AI-ready data architecture. This role conducts deep assessments of existing systems-including Banner, Workday
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, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap camera footage for wildlife monitoring and conservation efforts. Job Responsibilities
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to support the growth and competitiveness of our automotive designs in the global landscape. For more details, please view https://www.ntu.edu.sg/ancl Key job purpose Conduct investigations on the bonding and
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, we believe science can achieve its fullest potential. THE ROLE During your internship you will work on a projectin the Cultural Heritage Technologies (https://www.iit.it/it/web/cultural-heritage
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communication skills. Proficiency in developing deep learning models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in
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and inference of unmeasured observables. 2. Improve computational methods to extract the CKM matrix element Vub from inclusive decays using deep learning approaches. Environment: ICCUB is a María de
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Engineering in the 2025 QS World University Rankings by Subjects. The EEE Rapid-Rich Object SEarch (ROSE) Lab focuses on research in: (i) visual search & retrieval, (ii) video analytics & deep learning, and
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systems, advanced sensing technologies, application of deep learning and AI for wireless systems, etc. For more details, please view: https://www.ntu.edu.sg/eee We invite applications for the position
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: https://www.jobbnorge.no/en/available-jobs/job/294558/phd-research-fellow-in-deep-learning-for-imaging-of-marine-ecosystems Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/294558/phd
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Electronique, Energie, Automatique (EEA) ou équivalent. Le candidat doit posséder un bon niveau en mathématique et des connaissances en traitement du signal. Des connaissances en machine learning/deep learning