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[½»Á÷] ±±¾©Àí¹¤´óѧ×Ô¶¯»¯Ñ§ÔºSaša V. Raković½ÌÊÚ³ÏÕÐÓÅÐ㲩ʿÉú

ѧУ£¨University£©£º±±¾©Àí¹¤´óѧ£¨Beijing Institute of Technology, BIT£©

Ժϵ£¨School£©£º×Ô¶¯»¯Ñ§Ôº£¨School of Automation£©

ѧ¿Æ£¨Discipline£©£º¿ØÖÆ¿ÆѧÓ빤³Ì£¨Control Science and Engineering£©

Èëѧʱ¼ä£¨Entrance time£©£º2022ÄêÇï¼¾£¨Start in the Fall semester 2022£©

Ñо¿·½Ïò£¨Research direction£©£ºÄ£ÐÍÔ¤²â¿ØÖÆ¡¢È˹¤ÖÇÄÜÓë¿ØÖÆ£¨Model predictive control; Learning and artificial intelligence£©

ѧУ¼ò½é£¨About BIT£©£º
Beijing Institute of Technology (BIT) is one of the most important and successful engineering universities in China. In the 2018 QS University World Ranking, BIT ranked 389th in the world, 76th in Asia and the top10 in science and engineering in China. It has been one of the key universities in China since its founding and the first batch of universities which has entered the national ¡°211 Project¡±, ¡°985 Project¡± and the ¡°Top A World-class University¡±. BIT was rated as a ¡°double first-class university¡± and its control science and engineering is rated as a ¡°double first-class discipline¡± in 2017.

µ¼Ê¦¼ò½é£¨About Supervisor£©£º
Professor Saša V. Raković received the PhD degree in Control Theory from Imperial College London. His PhD research was awarded the Eryl Cadwaladr Davies Prize 2005 as the best PhD thesis in the EEE Department of Imperial College. He has been affiliated with a number of well-known universities, including, inter alia, Imperial College London, ETH Z¨¹rich, Oxford University, University of Texas at Austin, University of Maryland at College Park, and Texas A&M University at College Station. Saša V. Raković¡¯s main interests and contributions lie within the areas of synthesis of control systems, analysis of dynamical systems, and decision making under constraints and uncertainty. He is a pioneer of tube model predictive control. His work is published in top journals and it is highly cited, and he has also edited, with William S. Levine, a highly popular ¡°Handbook of Model Predictive Control¡± published by Springer Nature.

ÏêϸҪÇó£¨About Positions£©£º
The candidates should be prepared to work across a number of disciplines including at least controls, dynamics, optimization, and systems as well as artificial intelligence, learning and decision making. The candidates should be familiar with MATLAB or a closely related programming language. Solid knowledge of English is necessary.
A number of projects will be available. Two main research themes are theory and implementation of model predictive control under constraints and uncertainty as well as interactions of artificial intelligence and control theory. A number of projects is available with the focus on:
1. Theory, implementation, and application of optimal and model predictive control in general and tube optimal and model predictive control in particular.
2. Theory for symbiosis of learning and optimal and model predictive control as well as related implementation and application of the developed methods.
3. Theory, implementation, and application of adaptive optimal and model predictive control.

ÈçºÎÉêÇ루How to Apply£©£º
ÉêÇëµÄͬѧÇëÓÃÓ¢ÎÄдÓʼþ²¢ÌṩÏÂÁеÄÓ¢Îĸ½¼þ²ÄÁÏ·¢ËÍÖÁÓÊÏ䣺zhangsixing@bit.edu.cn£¬Ìõ¼þºÏÊʵĽ«»á½øÒ»²½ÁªÏµ½»Á÷¡£
1. CV that includes list of publications (if any),
2. Personal statement that includes details on why she/he would be ideal candidate for Ph.D. position within above specified focus,
3. List of taken lecture courses as well as a summary of grades/marks for taken courses.

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