www.acad.jobs : academic jobs worldwide – and the best jobs in industry
                
     
Position: Post-Doc Position in Deep Bayesian Robotics
Institution: ShanghaiTech University
Location: Pudong, Shanghai, China
Duties: This position is for an ongoing project which aims to teach robots to behave in our environment and to adapt their behavior to malfunctions/disturbances. Although the chosen tools and approaches are still flexible, we put a strong emphasis on research focused on 1. Parametric search with Bayesian Optimization, 2. Deep Bayesian Learning applied to Policy-search algorithms or 3. Model Predictive Control
Requirements: a PhD in robotics, computer science, mechatronics, mechanical engineering or related field by the start date; demonstrate interest in Reinforcement Learning algorithms and other learning/control methods; desirable experience modeling robots using a physics-engine/multibody dynamics simulator; proficiency in C++ or Python programming and experience developing real-time applications; excellent written and verbal English communication skills
   
Text: English 人力资源处 Jobs_english Post-Doc position in Andre Rosendo's research group (Deep Bayesian Robotics area), SIST 时间:2020-06-10浏览:10设置 A+ A- 夜晚模式 We are accepting applications for an open Post-Doc position in Deep Bayesian Robotics at ShanghaiTech University. This position is for an ongoing project which aims to teach robots to behave in our environment and to adapt their behavior to malfunctions/disturbances. Although the chosen tools and approaches are still flexible, we put a strong emphasis on research focused on 1. Parametric search with Bayesian Optimization, 2. Deep Bayesian Learning applied to Policy-search algorithms or 3. Model Predictive Control. We are looking to appoint skilled and enthusiastic engineers with programming experience (or a good mathematical foundation) and strong interest in experimenting with robots, and we have two generous funds to be used to buy whatever robot necessary for your research. You will be expected to work side-by-side with English-speaking graduate students at the Living Machines Laboratory, to read recent papers while implementing state-of-the-art algorithms, to comprehend and improve this state-of-the-art and, more importantly, conduct experiments to validate theories and algorithms. Requirements 1. a PhD in robotics, computer science, mechatronics, mechanical engineering or related field by the start date 2. demonstrate interest in Reinforcement Learning algorithms and other learning/control methods 3. desirable experience modeling robots using a physics-engine/multibody dynamics simulator 4. proficiency in C++ or Python programming and experience developing real-time applications 5. excellent written and verbal English communication skills Conditions of employment - Three year contract, with possibility of extension or reduction. We accept candidates from anywhere in the world, and we will provide all documents required for your work visa application. Employer: ShanghaiTech University, Computer Science Department ShanghaiTech University was founded in 2014 and is a research-focused university. One of the premises of the university is to keep a low student-to-teacher ratio (less than 12, the lowest in China) to keep faculty members focused on research. ShanghaiTech is financially backed by the Shanghai Government and the Chinese Academy of Sciences, enabling the university to invest large sums in state-of-the-art research. Additional information For information about this vacancy, you can contact Andre Rosendo, Assistant Professor, email: arosendo@shanghaitech.edu.cn . For those living in China, you could also contact Andre through WeChat (User id: arosendo) To apply, send an up-to-date curriculum vitae (cv), to arosendo@shanghaitech.edu.cn : External links: http://lima.sist.shanghaitech.edu.cn/ http://sist.shanghaitech.edu.cn/sist_en/2018/0820/c3846a31763/page.htm Copyright © 上海科技大学 版权所有 返回原图 /
Please click here, if the job didn't load correctly.







Please wait. You are being redirected to the job in 3 seconds.