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Field
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | about 1 month ago
: Electrotechnical and Computer Engineering. Admission requirements: Students enrolled in a PhD. program in Electrotechnical and Computer Engineering, or in related areas, or alternatively, an MSc degree in
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the partners of NorwAI , but also with PhD candidates and other postdoctoral fellows at the center and the department as a whole. Duties of the position Explore novel techniques for pre-training, fine-tuning and
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
is important to note that this position is contingent on T32 funding. Education and Experience: PhD in Computer Science, Bioinformatics, Computational Biology, or related discipline. Knowledge of
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analysis. Required Qualifications* A PhD in hydrology, water resources engineering, environmental science, civil engineering, atmospheric sciences, computer science, or a related field is required either
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hydrologic modeling and analysis. A PhD in hydrology, water resources engineering, environmental science, civil engineering, atmospheric sciences, computer science, or a related field is required either
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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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on complex system subject to various constraints. Design and develop real-time algorithms. Simulate and evaluate system performance. Document research outcome to publish at international conference / journal
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and