Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- Oak Ridge National Laboratory
- Duke University
- European Space Agency
- Fudan University
- Institute of Mathematics and Informatics
- Los Alamos National Laboratory
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Michigan State University
- Ohio University
- RIKEN
- Rutgers University
- THE UNIVERSITY OF HONG KONG
- Technical University of Munich
- UNIVERSITY OF HELSINKI
- UNIVERSITY OF VIENNA
- University of Arizona
- University of California, Merced
- University of Helsinki
- University of Lund
- University of Oklahoma
- Yale University
- 11 more »
- « less
-
Field
-
: - Quantum computing with qudits, quantum error correction and fault-tolerance - Quantum optics of trapped ions and Rydberg atom arrays - Numerical tensor network techniques - Topological order and (de
-
algorithms, error correction, or many-body quantum systems. o Proficiency in numerical simulations (e.g., tensor networks, quantum circuit modeling). · For Experimentalists: o PhD in atomic/molecular/optical
-
. Experience in phase-contrast and/or dark-field x-ray imaging. Experience in working with photon-counting x-ray detectors Experience with scanning x-ray imaging such as SAXS mapping, SAXS tensor tomography
-
learning and tensor libraries such as PyTorch We Are Delivering Scientific Excellence Los Alamos National Laboratory is more than a place to work. It is a catalyst for discovery, innovation and achievement
-
functional data analysis, tensor regression, high-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated
-
Science and Quantum Computation Tensor Learning Team , Generic Technology Research Group, RIKEN Center for Advanced Intelligence Project (Team Leader: Qibin Zhao) Functional Analytic Learning Team
-
-order differentials. Differentiable wind models for accurate re-entry simulations. High-order State Transition Tensors usages and efficient computation. Manoeuvre detection and estimation of non
-
Mathematical Physics in the last 40 years. Researchers in this area will be expected to have knowledge of the aspects of Quantum Groups associated to Representation Theory, Tensor Categories, Poisson Geometry
-
with tensor networks, AI in physics and materials, etc.). A Ph.D. in Physics or a closely related field is required. The initial appointment will be for one year, and is renewable for another one or two
-
or novel applications of machine learning. Expertise in deep learning techniques such as transformers, LLM, GNN, generative models OR advance matrix method such as matrix/tensor completion, non-negative