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Description The Deep Learning laboratory in the Division of Science, New York University Abu Dhabi, seeks to recruit a research assistant to work on Deep Reinforcement Learning (DRL). The successful
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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experimental design. Hands-on experience with classical machine learning methods such as linear/logistic regression, decision trees, and gradient boosting. Familiarity with deep learning concepts and modern
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the competent authority of the Ministry of Higher Education, Research and Innovation (MESR). "Video content security in a deep learning coding architecture" Over the past few decades, numerous video compression
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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interdisciplinary team with clinicians and engineers; You have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and
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and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators. Develop interpretable models
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deep learning, on the topic “Analysis and Reconstruction of Digital Data Fragments”. This internship is intended for students at M2 level, or in the final year of an engineering school, interested in a