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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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computing subjects, including artificial intelligence. In addition, you will be able to demonstrate specialist expertise in one or more of the following areas: - Machine learning and deep learning
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Priorities: We seek applications across all AI domains, with emphasis on: Foundational AI : Machine Learning, Computer Vision, NLP, Robotics & Embodied Intelligence, Data Science. Interdisciplinary Frontiers
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relational database environments Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses) Apply machine learning approaches for predictive modeling
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Qualifications: The successful candidate must hold a Doctorate/PhD degree or equivalent in machine learning or closely related field Experience with teaching on university level Strong background in machine
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of industrial processes. In a joint effort of both institutes, the Department AI4Quantum – Machine Learning for Quantum Simulation and Computing and Thermal Energy and Process Engineering are looking for a PhD
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» Computer engineeringEducation LevelPhD or equivalent Research FieldEngineering » Communication engineeringEducation LevelPhD or equivalent Skills/Qualifications PhD (or equivalent) in computer and
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Qualifications: Completed doctoral studies – PhD in bio-resource technology, practical implementation of Machine Learning, or a related field. Strong knowledge of Food security theory. Understanding of principles
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aiming to pursue either PhD, MD, or combined MD/PhD programs as their next steps. The successful applicant will have advanced experience in one or more of the following areas: molecular biology, cell
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About the Opportunity The Lecturer will teach introductory courses in architectural drawing, sketching, studio design, computer modeling, architectural history, technology, or project case studies