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Mathematics, or a related field A strong background in image/signal processing, particularly in computer vision. Strong programming skills and experience with at least one deep learning framework e.g
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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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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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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Researcher (R1) Positions PhD Positions Application Deadline 31 Jul 2026 - 14:01 (Africa/Abidjan) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 2 Nov 2026
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, KELIM score), mutational profiles, histopathological information, and long-term survival outcomes. The first objective is to implement automated deep-learning–based segmentation of primary ovarian tumors
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impactful system capable of reconstructing the 3D fetal aortic arch from routine 2D ultrasound views by combining generative modelling, deep learning, and rigorous clinical validation. Working within a
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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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include the development of finite elements methods, as well as inverse design strategies based on deep-learning and Neural Networks approaches. The latter will then bring the project to the experimental
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solutions that enhance ecological monitoring, improve resilience planning, and promote sustainable resource management. Development of a Detection Transformer through Attentive Deep Learning and Explainable