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learning architectures for scientific or high-performance computing applications. Background in software performance evaluation, profiling, and optimization on CPUs and GPUs. Knowledge of common numerical
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date). Strong background in power systems analysis (OPF, state estimation) and numerical optimization/control. Proficiency with Python/MATLAB and power-system toolchains (e.g., MATPOWER/OpenDSS
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction
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Mathematics, or a closely related field. Design and optimize multimodal LLMs to encode, fuse, and reason over heterogeneous scientific data from diverse modalities such as numerical tables, text, and images
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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Associate on the track of Smart Integrative Energy Systems will participate in the research efforts of developing systems integration, analysis, design, control, and/or optimization models and algorithms
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resources and demonstrated ability in applying numerical techniques to water-energy research. Strong candidates will have advanced knowledge and skills relevant to one or more of the following areas: River
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Science, or a related field Strong programming skills in Python, R Solid understanding of: Machine learning fundamentals Deep learning architecture (e.g., CNNs, RNNs, Transformers) Optimization and model
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
. For these reasons, most spacecraft operations so far have relied on human in-the-loop. However, the opportunities and advantages for AI deployment are very significant, including the ability for fleets of numerous