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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and
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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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research potential. Candidates will be considered with background in the areas of hyperbolic PDEs and optimization problems. Expertise in numerical simulations (with Matlab or Python) will be valued
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-accurate-portable-diagnostics/. What you’ll do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries
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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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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