Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Oak Ridge National Laboratory
- EPFL
- NEW YORK UNIVERSITY ABU DHABI
- AALTO UNIVERSITY
- MOHAMED BIN ZAYED UNIVERSITY OF ARTIFICIAL INTELLIGENCE
- SciLifeLab
- Stanford University
- University of North Carolina at Chapel Hill
- Utrecht University
- Yale University
- ;
- Aarhus University
- Argonne
- Baylor College of Medicine
- Eindhoven University of Technology (TU/e)
- Forschungszentrum Jülich
- Fundació per a la Universitat Oberta de Catalunya
- Harvard University
- Inria, the French national research institute for the digital sciences
- Istituto Italiano di Tecnologia
- KU LEUVEN
- Lehigh University
- Max Planck Institute for Gravitational Physics, Potsdam-Golm
- Mohamed bin Zayed University of Artificial Intelligence
- Northeastern University
- Sandia National Laboratories
- Stony Brook University
- Texas A&M University
- University of California
- University of California, Los Angeles
- University of Florida
- University of Minnesota
- University of South Carolina
- 23 more »
- « less
-
Field
-
architecture of entirely new foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across
-
foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
-
seeking applicants for a full-time postdoctoral scholar position in Computational Immunology and Genomics. The successful candidate will develop and apply computational, statistical, and machine-learning
-
experience with machine learning or deep learning; experience with PyTorch is desirable. Experience with large structured, unstructured, imaging, or multimodal datasets and, where relevant, GPU-accelerated
-
Associate position focusing on control systems engineering, artificial intelligence (AI), and scientific machine learning (SciML) applied to nuclear fusion energy. The successful candidate will join the
-
machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
-
, computer science, or engineering within the past 5 years. Previous theoretical and/or computational research experience in tensor networks, Monte Carlo, machine learning or a related field Proficiency in quantum
-
(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
-
. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor devices or metrology. We offer We offer a fully funded
-
(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor