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University of North Dakota
  • Employee Resources
    • Job Openings
    • Benefits
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    • Staff Senate
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  • Anjali Sandip
Anjali Sandip

Anjali Sandip

Ph.D.
  • Teaching Assistant Professor, Mechanical Engineering

Contact Info

  • Email: anjali.sandip@UND.edu
  • Office: 701.777.6855

Office Address

Upson II Room 160E
243 Centennial Drive Stop 8359
Grand Forks, ND 58202-8359

Websites

  • Visit Website
  • Google Scholar

Biography

Anjali Sandip is a Teaching Assistant Professor in the Mechanical Engineering Department at the University of North Dakota. Her research interests include computational mechanics, high-performance computing, and machine learning. Anjali’s PI-led research projects have received support from NSF, DOE, and NVIDIA. Before her current position, she was a post-doctoral researcher at the University of Nebraska, where she developed patient-specific computational models of the superficial femoral artery/ stent interaction to treat peripheral artery disease. Anjali serves as a mentor for both undergraduate and graduate researchers. She is an active member of the Association of Computational Mechanics (USACM & IACM) and has delivered numerous presentations and published journal articles. Anjali has bachelor’s, master’s, and doctoral degrees in mechanical engineering.

ME 101 Introduction to Mechanical Engineering

ME 341 Thermodynamics

ME 323L Machine Component Design Laboratory

ME 529 Advanced Finite Element Methods

ME 590 Modeling Glaciers and Ice Sheets

ENGR 201 Statics

ENGR 340 Engineering Ethics

  • Applied Mechanics (solid/fluid/bio-mechanics)
  • Numerical methods
  • High-performance computing
  • Uncertainty quantification [UQ]
  • Physics-informed machine learning

NSF EPSCoR Research Fellow, 2024-25

Doctor of Philosophy, Mechanical Engineering, University of Kansas, Lawrence, USA


Master of Science, Mechanical Engineering, University of Kansas, Lawrence, USA

Bachelor of Engineering, Mechanical Engineering, Osmania University, Hyderabad, India.

I am actively seeking graduate students (Master's and Ph.D.) interested in working in the interdisciplinary fields of computational mechanics, applied mathematics, scientific machine learning, and earth sciences. If interested, please email me your CV and official transcripts with the subject line "Prospective graduate student".

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