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Open Postdoctoral position, faculty mentor Eric Darve

Important Info

Faculty Sponsor (Last, First Name): 
Darve, Eric
Stanford Departments and Centers: 
Mechanical Engineering
Postdoc Appointment Term: 
For 2 years, ending in September 2025.
Appointment Start Date: 
5/1/2023
Group or Departmental Website: 
http://insieme.stanford.edu
How to Submit Application Materials: 

Email the required application materials to darve@stanford.edu and doostan@colorado.edu.

Does this position pay above the required minimum?: 
Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.
Pay Range: 
$68,238-$80,000

Stanford University and the University of Colorado, Boulder have multiple immediate openings for postdoctoral positions in scientific machine learning. The successful candidates will join the INSIEME PSAAP Center at Stanford and work with Profs. Eric Darve and Alireza Doostan, alongside a team of experts from both institutions.
 
The primary focus of the positions is to conduct fundamental research in the areas of uncertainty quantification, data-driven modeling, and machine learning, with a specific emphasis on multi-fidelity modeling, deep neural networks, transfer learning, generative modeling, and inverse problems.
 
The positions are initially for two years, subject to annual review at the end of the first year. The successful candidates will work on large-scale, multi-physics modeling of complex systems and their HPC implementations.

Required Qualifications: 
  • Applicants must possess a Ph.D. in areas related to engineering or computational sciences, with experience in at least one of the above-mentioned areas.
Required Application Materials: 
  • CV
  • A one-page research statement
  • Contact details of two references

 

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.