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scattering with machine-learning models trained on plant mutants, the project will shed new light on the cellular-level biochemistry that governs plant growth, development, and stress responses. This is a
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.; - Develop skills in artificial intelligence and machine learning techniques for analyzing operational data and detecting anomalies, using foundational model approaches (e.g., GridFM project, LF Energy
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning
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solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical, IoT systems. Key
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | 9 days ago
. Candidates should possess a strong background in power systems, multi-objective optimization and control (particularly MPC), and machine-learning–based time-series forecasting, along with proficiency in MATLAB
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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, machine learning, and life cycle assessment, we aim to create sustainable wearable systems to enhance human well-being. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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of subsurface processes. You will be responsible for leading the development of the approach, which could include transferring learning from other geographic regions and data types, machine learning methods