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at international academic gatherings; Create, maintain, and document high-quality research code for reproducibility; Maintain good practice in managing and accessing sensitive medical datasets; Assist the supervisor
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technologies and data sources; as well as the combination of traditional traffic flow theory concepts with new empirically derived models and data science ideas. Applicants must have received a PhD in
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trajectories and enhance interaction safety and robustness. Learning for Human-Robot Interaction: Integrate machine learning techniques, such as reinforcement learning and generative models, with control theory
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Division, New York University Abu Dhabi, seeks to recruit a post-doctoral associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and
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-edge machine learning, including Large Language Models (LLMs), to enhance decision-making and planning in robotic systems. Qualifications: Applicants must have a PhD in Robotics, Control Theory
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. The project activities will involve the development of the theory and implementation of the advanced mechanics and numerical models as well as constitutive model calibration and validation based on physical
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Research / Post-Doctoral Associate in the Division of Science Computer Science, Dr. Djellel Difallah
include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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multiple projects in urban sociology, inequality and social networks, broadly construed. The successful applicant will contribute to one or more interdisciplinary projects. Example projects include the study
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, coding pipelines for optical imaging/photometric data analysis, and spectral modelling, are strongly encouraged to apply. Successful candidates will be encouraged to develop independent projects in
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collaboration between multiple research groups and involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health