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Field
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the ocean based on satellite imagery. A key focus will be to develop oil spill segmentation approach based on optical sensors, and how this can be integrated with SAR sensors, current satellite imagery
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dynamics under different measurement scenarios. Software implementation of sensor fusion and data-driven evaluation methods. Economic and risk analyses to quantify the value of various measurement strategies
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scalable quantum algorithm development and quantum-HPC codesign. What is Required: PhD in Computer Science, Computational Science, Applied Mathematics, or a related field awarded within the last five years
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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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technologies used by the Center which may include, but not be limited to thermal cameras, audio recorders, air and other environmental samplers, and environmental sensors. In addition, this position will
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validate advanced 5G features such as network slicing, MEC and xApp/rApp. Contribute to the development of innovative solutions and algorithms to enhance 5G network capabilities. Work closely with
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, TensorFlow) and Python ML libraries (e.g., NumPy, OpenCV, scikit-learn). Experience implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based
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implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based approaches. Proven ability to design and run rigorous experimental frameworks, including
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eHC through reduced scale testing. Lastly, you will also need to evaluate the various gas monitoring sensors performance in detecting the battery gas releases for remote monitoring and early detection
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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms