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. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized
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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications should include a CV, a list of publications and a research statement. Applicants should also
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, United States of America [map ] Subject Area: Engineering / Quantum Science and Engineering Appl Deadline: (posted 2025/12/19, listed until 2026/06/19) Position Description: Apply Position Description The Duke Quantum
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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dwarfs and/or the high-precision RV survey of solar-type stars being conducted with the NEID instrument on the WIYN 3.5m telescope; (2) improving technology, algorithms, and data analysis to mitigate
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inference in engineered systems, including telecom networks; The development of neuromorphic algorithms and spiking neural models with built-in efficiency and reliability guarantees; The design of reliability
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decision-making algorithms on real robotic systems operating in unstructured and dynamic environments. This work is connected to the Robotics Institute Germany (RIG) and relates to the thematic cluster