Sort by
Refine Your Search
-
Country
-
Employer
- Lawrence Livermore National Laboratory
- Forschungszentrum Jülich
- Aarhus University
- Aarhus University (AU)
- European Space Agency
- Monash University
- Northeastern University
- Rutgers University
- Stanford University
- University of California, Los Angeles
- University of Central Florida
- University of Copenhagen
- University of Manchester
- University of Texas at Arlington
- University of Texas at Austin
- Vanderbilt University
- Yale University
- 7 more »
- « less
-
Field
-
diagnostics. Analyze data to contribute to validation of theoretical models and simulations for fusion devices. Document and publish research results in peer reviewed scientific or technical journals and
-
& OD system, Oracle Fusion HCM. This role is central to ensuring our organisational structure, reporting lines and Position data remain reliable, up to date and aligned with Finance systems and
-
’ distribution shifts via “data-model fusion”. The successful candidate will develop near-real-time integration between emerging environmental and ecological AI-driven data sources (e.g., automated acoustic and
-
approaches • Integration of proteomics with genetic data via MR, coloc and FUSION to identify causal and druggable targets Requirements • The successful applicant will hold, or shortly expects to obtain
-
solid-state lasers, high peak power lasers, high average power lasers, and high energy / high intensity short pulse lasers. This work will take place within the Extreme Light, Matter, and Fusion (LMF
-
computational postdoctoral fellow to lead core deep-learning work across surgical computer vision, multimodal fusion, and clinical translation. Project overview You will advance funded work that
-
Cardiotoxicity Risk Prediction Among Breast Cancer Survivors Using Multi-Site Real-World Data: https://www.fda.gov/about-fda/oncology-center-excellence/cardioonco-ai-ai-empowered-cardiotoxicity-risk
-
Start Date: As Soon As Possible Group or Departmental Website: https://med.stanford.edu/gruberlab.html (link is external) How to Submit Application Materials: Interested applicants should submit by
-
computing Experience in the development and application of process-based models and/or model–data fusion and data assimilation methods Very good programming skills in Python and/or Fortran; knowledge of
-
) and structural engineering performance in spent fuel Bridge the gap between theory and experiment by integrating microstructural and experimental data into computational models to validate predictive