Control Robotics Identification and Signal Processing(CRISP) laboratory at Tufts is looking to fill a postdoctoral research position in the broad areas of machine learning, reinforcement learning, and optimization theory/methods. There is an opportunity to select a project that is theory oriented, experimental/software-focused, or a combination of both.
Theoretical investigations include but are not limited to the following topics:
Decentralized optimization with a focus on stochastic and non-convex problems
Kalman filtering, nonlinear filtering, and high-dimensional statistics
Optimal sensor placement and reinforcement learning
Experimental focus includes the following aspects:
Practical implementation of optimization algorithms: decentralized and parallel training and learning of machine learning models on multiple core architectures and GPUs
AI-based learning and control of single robot and teams of robots
Multi-UAV and ground robot localization, navigation, and control
Successful candidate must
hold a Ph.D. in signal processing, machine learning, control theory, or in a related area,
be highly motivated and able to pose/investigate research problems independently,
have an established track-record of publications at top academic venues,
be strong in Python and related programming languages with experience/exposure to PyTorch, TensorFlow, and Deep Learning.
This is an excellent opportunity to build an independent research portfolio necessary for top academic or industrial positions. The contract duration is initially for one year with potential to extend. There is flexibility to travel, attend conferences, and give professional seminars/talks.
Submit a detailed CV, cover letter, research statement, teaching statement, two sample publications, and two reference letters via the Interfolio system
For more information, please contact
Usman Khan, Ph.D.
Associate Professor, Electrical and Computer Engineering
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