Post-doctoral Fellowships in Machine Learning/Numerical Analysis in Geotechnical Engineering at SDU

Post-doctoral Fellowship positions in Machine Learning/Numerical Analysis in Geotechnical Engineering are open at the Geotechnical Engineering Research Group, Shandong University, Jinan, China. Candidates should have graduated with a Ph.D. within the last three years and have experience in machine learning/numerical analysis in geotechnical engineering. Successful candidates will engage in interdisciplinary research in applying machine learning/numerical analysis in transportation geotechnics, underground space or offshore geotechnical engineering. Interested applicants are encouraged to contact Prof. Kai Yao https://www.researchgate.net/profile/Kai-Yao by emailing yaokai@sdu.edu.cn The salary package could be referred to https://www.en.sdu.edu.cn/info/1169/7271.htm The Required Qualifications: 1. Legal Compliance: Candidates must comply with Chinese laws and regulations, be physically and mentally healthy, and have no criminal record, 2. Age Requirement: Must be 35 years old or younger, 3. Educational Background: Must hold a Ph.D. in Geotechnical Engineering, Civil Engineering, Geological Engineering, or a closely related field, obtained within the last three years, or be on track to complete their Ph.D. soon, 4. Commitment: Must be able to conduct a full-time postdoctoral research at Shandong University, 5. Experience in numerical analysis in geotechnical engineering, 6. Proficiency in programming with Python, MATLAB, or Fortran. The Application Process: ➢ Application Materials: 1. CV (with a list of publications) 2. 1-page cover letter (please explain how you fit into this position; please also clearly indicate in the letter that you meet the requirements for points 2 and 3 listed under 'The Required Qualifications.') ➢ How to Apply: Email your application materials to Prof. Yao at yaokai@sdu.edu.cn ➢ Deadline: May 31, 2026 (Only shortlisted candidates will be contacted) ➢ Starting date for the position: at the earliest convenience.