鲍桂帆

主要研究方向为控制学、电气工程及机械工程。
      
唐教授博士期间师从控制学界大拿Dr. Miroslav Krstic，于业界权威学术期刊Automatica以及IEEE TAC发表多篇论文，对科研及教学充满热忱，工作严谨，待人和善。望各位有志从事相关领域研究的学弟学妹积极考虑，招生信息详见本文附件。

Two Ph.D. student position openings are available in Dr. Shu-Xia Tang’s Control Systems Laboratory, 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. More research information can be found at https://www.shuxia-tang.net/.

Dr. Shu-Xia Tang is currently an assistant professor at the Department of Mechanical Engineering, Texas Tech University, USA. She 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, (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).