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Open Postdoctoral position, faculty mentor Alison Marsden

Important Info

Faculty Sponsor (Last, First Name): 
Marsden, Alison
Other Mentor(s) if Applicable: 
Daniele Schiavazzi (Notre Dame)
Stanford Departments and Centers: 
Institute for Computational and Mathematical Engineering
Postdoc Appointment Term: 
1 year, renewable
Appointment Start Date: 
July 1, 2022
How to Submit Application Materials: 

To apply, submit the required application materials as a single PDF to

We seek applications for an open postdoctoral position available in the research group of Prof. Alison Marsden at Stanford University, in collaboration with Daniele Schiavazzi at Notre Dame, in the broad research area of model-based inference with applications to cardiovascular hemodynamics. The candidate will be responsible to conduct methodological and applied research, participate and help to organize research group meetings focusing on the solution of direct and inverse problems in uncertainty analysis and the construction of multi-fidelity surrogate models.
We are seeking a highly motivated and driven applicant with strong writing and communication skills, a strong background in applied probability and uncertainty quantification, computational fluid/solid mechanics, coding in C++/Python, and the ability to work independently and collaborate effectively. Previous experience with parallel programming (CPU/GPU) and development of deep learning neural networks in PyTorch is a plus.
The position will initially be for one year with the possibility of extension depending on performance.
The evaluation of candidates will begin immediately and continue until the position is filled.

Required Qualifications: 
  • PhD in Computational Science, Applied Math, Mechanical Engineering or related field
Required Application Materials: 
  • Cover Letter
  • Curriculum Vitae
  • Three Reference Letters


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.