-
the last five years. Experience of AI and efficient computing. Strong programming skills Familiarity with popular Deep Learning platforms such as PyTorch and TensorFlow Preferred Qualifications: Expertise in
-
learning conferences and journals. Be a part of a collaborative research environment which will provide the opportunity to perform cutting-edge research in deep learning and scientific computing. Deliver
-
mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design
-
Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi-scale coupled physics simulations in support of the missions and programs of the US
-
engineering, architecture, architectural engineering, or related field completed within the last five years. Experience in building energy modeling and analysis. Deep understanding of building thermal physics
-
from plant genomics to phenomics with biological mechanisms embedded in deep neutral networks. GPTgp will allow task-specific training and transfer learning across reactions, pathways, biodesigns, and
-
, high performance computing and deep learning. The candidate will work in a collaborative research and development environment focusing on designing, implementing, and applying robust and high performance
-
(e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection. Advance algorithms for multi-modal tomography (X-ray, neutron, electron