Joining Our Group

(For Ph.D. Program Applicants)

I am looking for two Ph.D. students (the anticipated starting term is 2023 Spring/Fall) in general research areas of Scientific Machine Learning (SciML), Data Assimilation, and Uncertainty Quantification with the focus on (i) developing novel data-driven modeling and simulation techniques, and (ii) applying them to building digital twins for engineering applications (e.g., renewable energy systems, advanced manufacturing, autonomous systems). Students will be supported by research assistantships that cover the full tuition and stipend. For those interested, please send your CV and transcripts to jinlong.wu@wisc.edu.

 

Required qualifications:

  • Bachelor/Master's degree in engineering, applied math, or a related field.

 

Preferred qualifications:

  • Passionate about developing novel methods of SciML and applying them to engineering applications.

  • Strong coding skills in one or more programming languages (Python/Julia/C/C++).

  • Solid background in undergraduate-level math and statistics courses.

  • Previous experiences with research projects of computational fluid dynamics (or projects of computational mechanics).

  • Previous experiences with one or more topics in Scientific Machine Learning, Data Assimilation, Uncertainty Quantification, Inverse Problem, Reduced Order Modeling.

  • Proficiency in English reading, writing, and speaking.

The University of Wisconsin-Madison is a top-ranked research institution located on the south shore of Lake Mendota. According to an article in Nature​, UW-Madison is ranked as No.4 among U.S. universities in terms of producing faculty in the past decade. In the 2023 edition of U.S. News & World Report’s "Best Graduate Schools", our ME program is ranked 14th (five-way tie).

(For Undergraduate/Master Students)

Students at UW-Madison seeking undergraduate/master research projects in the areas of data-driven modeling and simulation in engineering applications are also very welcome. Please send your CV and transcripts to jinlong.wu@wisc.edu.