-
-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
-
relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
-
experience in deep learning frameworks (TensorFlow/PyTorch) Experience with large-scale genomic/proteomic datasets and machine learning applied to biological sequences Knowledge of phylogenetics, protein
-
calculations, reactive empirical force fields, chemical dynamics, deep learning and numerical algorithms, data analysis, experimental characterization and imaging. Our research has involved methodology and
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
-
turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and
-
with a team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience in machine learning/deep learning methods
-
with computer-aided design software. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and
-
AI hardware to help solve significant real-world problems using machine learning and deep learning. ALCF researchers work in a highly collaborative environment involving science application teams
-
for future Exascale architectures. All candidates are encouraged to apply if they have a genuine interest learning and enhancing these skills. Candidate is looking forward to work and engage with a diverse