Sort by
Refine Your Search
-
Listed
-
Employer
- Virginia Tech
- Cornell University
- Argonne
- Northeastern University
- Oak Ridge National Laboratory
- Texas A&M University
- The Ohio State University
- Brookhaven National Laboratory
- Duke University
- Lawrence Berkeley National Laboratory
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology (MIT)
- National Aeronautics and Space Administration (NASA)
- North Carolina A&T State University
- Pennsylvania State University
- SUNY University at Buffalo
- South Dakota Mines
- University of California, Merced
- University of Central Florida
- University of Miami
- Zintellect
- 11 more »
- « less
-
Field
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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