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particular: Design algorithm for localization using ultrawide band (UWB) sensors. Conduct experiment, collect data, and analyze results. Simulate and evaluate system performance. Document research outcome to
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algorithms Engaging in scientific exchange with collaboration partners of the project Preparing reports, scientific papers, and presentations Project duration: 6 months Job Requirements: Master’s degree in
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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Responsibilities: The Research Associate is responsible for Formulating new algorithms for sampling from conditioned SPDE models Developing a mathematical theory showing their correctness and reliability Find
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quantum sensing technologies (e.g., Rydberg atomic sensors) for wireless communications and sensing. Key Responsibilities: Develop quantum-related theories, models, and algorithms for various communications
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Research Associate (Computer Engineering/Computer Science/Engineering/Mathematics/Biology/Chemistry)
. Key Responsibilities: Develop deep learning models and algorithms for biomedical and healthcare applications. Data processing for training of deep learning models. Mentor junior research lab members in
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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