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Field
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to support a two-year Small Business Technology Transfer project (STTR) funded by the National Science Foundation. The successful candidate is expected to support the development, simulation and
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the evaluation of converter and drive architectures for edge-resident intelligence; the development of advanced modulation, estimation and control algorithms for high-performance operation; and the integration
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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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systems, understand the design and development decisions that propagate social biases, and develop theoretical and algorithmic approaches to mitigate them. Key responsibilities include developing bias
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algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research group's efforts in modeling combustion-generated aerosols. These modeling framework will be used to understand
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-guided) Evolutionary trajectory analysis and fitness landscape modeling Integration of predictive algorithms with experimental iteration cycles High-throughput screening and selection platform development
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routine background checks. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms
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projects, including the flagship EXCELSIOR H2020 Teaming Project (https://excelsior2020.eu/ ), as well as DIGIFARM, NOSTRADAMUS, CERBERUS, and CARBONICA. ERATOSTHENES Centre of Excellence is an autonomous
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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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, such as new topological states, magnetism, electrical/thermal transport, superconductivity, and corresponding material predictions. (2) Develop numerical algorithms and computing software, such as