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美国Texas Tech University机械工程系Dr. Shu-Xia Tang实验室招收全额奖学金博士生

知社 知社学术圈 2022-09-22

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Two Ph.D. student position openings are available in Dr. Shu-Xia Tang’s Control Systems Laboratory at the Department of Mechanical Engineering, Texas Tech University, USA, anticipated to start around August 2020. Fully-funded scholarship is available to cover both tuition and living expenses. The successful candidates will work on research projects in the fields of battery management systems or/and swarm robotic systems, and can concurrently work on water management systems, oil drilling systems, traffic management systems, 3D printing systems subject to the candidates’ own interest. Visiting students and visiting scholars are also welcome to apply. More research information can be found at https://www.shuxia-tang.net/.  

Dr. Shu-Xia Tang is an IEEE senior member and is an IEEE CSS (Control Systems Society) Technical Committee member on Distributed Parameter Systems. She serves as an associate editor of Journal of Control, Automation and Electrical Systems and as an IEEE CSS Conference Editorial Board member. Her main research interests are stability analysis, estimation and control design of distributed parameter systems.

Interested candidates should send a CV detailing academic achievements to Dr. Shu-Xia Tang at shuxia.tang@ttu.edu. All applicants must satisfy Mechanical Engineering graduate program admission requirements with good GPAs, and international applicants must obtain satisfactory TOEFL/IELTS scores and acceptable GRE scores. Dedicated and self-motivated candidates are in particular encouraged to apply:

  • M.S. degree in mechanical engineering, electrical engineering, computer sciences, (applied) mathematics, or related areas (required);

  • Hands-on experience in experimental testing or/and hardware design (required);  

  • Expertise in MATLAB/SIMULINK or Python (required);

  • Excellent mathematical skills (preferred);  

  • Excellent oral and written communication skills (preferred);

  • Strong skills in control and optimization (preferred).


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