Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Michigan State University
- City University of Hong Kong (Dongguan)
- The University of Alabama
- AALTO UNIVERSITY
- Aalto University
- Argonne
- ETH Zürich
- King Abdullah University of Science and Technology
- Lawrence Livermore National Laboratory
- Northeastern University
- SUNY University at Buffalo
- Texas A&M University
- University of Vienna
- ARCNL
- Arizona State University
- Baylor University
- Boise State University
- Brown University School of Engineering
- CNRS
- Chosun University
- Delft University of Technology (TU Delft)
- Howard Hughes Medical Institute
- KTH Royal Institute of Technology
- NIST
- Nanyang Technological University L J Wong Group
- OSU
- Raven51 AG (BAM)
- Rochester Institute of Technology
- SCHOOL OF ENGINEERING, WESTLAKE UNIVERSITY
- Swiss Federal Institute of Technology Lausanne, EPFL
- Technical University of Denmark
- Technical University of Denmark (DTU)
- Technical University of Munich
- The Ohio State University
- The University of Chicago
- University of Amsterdam (UvA)
- University of California
- University of Delaware
- University of Texas at Austin
- Uppsala universitet
- 30 more »
- « less
-
Field
-
data-driven methods to understand, design, and optimize complex energy systems and devices. Example topics include physics-informed machine learning, digital twins for turbines and reactors, AI-driven
-
to understand how materials deform, degrade, break, and ultimately fail. Our research is driven by curiosity about the physical mechanisms that underlie failure and by the ambition to translate this
-
-preserving, machine-learning–accelerated scientific computing for plasma physics applications. In particular, the project involves developing data-driven collisional kinetic models and numerical schemes
-
.7.044603 2) " Magnon-phonon hybridization in 2D antiferromagnet MnPSe3 " https://www.science.org/doi/10.1126/sciadv.abj3106 3) " Recent progress in the JARVIS infrastructure for next-generation data-driven
-
mechanics of fragility in collagen, the mechanics of earthquakes, fracture of soft materials, and modeling failure in multiphysical processes such as corrosion-driven degradation of concrete. What unites
-
in free-electron-driven X-ray nanophotonics– i.e., modeling the physics of free electrons impinging on nanomaterials like van der Waals crystals, and subsequent coherent X-ray emission and imaging
-
chemical engineering. Areas of emphasis may include, but are not limited to, data-driven process modeling and simulation of complex, large-scale systems; AI-accelerated materials design, including
-
und Stellenangebote > Academic staff > Ph.D Position on Machine Learning in Biomechanics (m/w/d) Back to News Board Browse in News Professorship of Data-driven Materials Modeling Ph.D
-
position in Physics and Astronomy will work at the intersection of physics, astrophysics, biotechnology, materials science, computational and experimental modeling, and artificial intelligence (AI
-
Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials