A postdoctoral researcher position is available under an AFOSR-sponsored project on perception-based motion planning (PI:Takashi Tanaka). We are particularly interested in candidates with expertise in (i) motion planning and SLAM; (ii) machine learning with emphasis on image recognition and perception; or (iii) networked control systems.

The expected start date is after June 1, 2021 (the exact start date is negotiable). The appointment is for one year, with possible extensions depending on research performance and availability of funds. The salary will be determined based on the standard postdoctoral salary rate of the University of Texas at Austin Cockrell school of Engineering.

We accept applications by email (ttanaka@utexas.edu) with the following attachments: 1) a curriculum vitae, 2) two representative publications, and 3) contact information for two references. The application deadline is June, 30, 2021.

We will present the following papers in the upcoming American Control Conference @ New Orleans.
– M. Hibbard, K. Tuggle and T. Tanaka, “Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes”
– J. Suh and T. Tanaka, “Encrypted Value Iteration and Temporal Difference Learning over Leveled Homomorphic Encryption”
– H. Jung and T. Tanaka, “Optimal Data Rate Allocation for Dynamic Sensor Fusion over Resource Constrained Communication Networks”
– A. Pedram, J. Stefan, R. Funada and T. Tanaka, “Rationally Inattentive Path-Planning via RRT*”

We accept Ph.D. students in the area of control, autonomy and robotics. The research encompasses topics of perception-based path planning, multi-agent reinforcement learning, and networked control systems. The research tasks will put more emphasis on the fundamental theoretical developments rather than on software/hardware implementations. The positions will be appointed as Graduate Research Assistants (GRA), Teaching Assistants (TA), or a combination of both, which will include a stipend and a full tuition coverage.

Qualifications: The applicant must also have good communication skills (written and oral) as well as proficiency in mathematical arguments and proofs. Students with background in optimization, robotics, and control theory are encouraged to apply.

How to apply: All applications must be made through the Cockrell School of Engineering’s admission process. Applicants should express their intentions to work with us in the Statement of Purpose essays.

We are looking for a qualified postdoctoral researcher with expertise in the following or adjacent fields:

1. Machine learning: Candidates should have a significant research experience in perception, image recognition, and machine learning algorithms in general.
2. Robotics: Candidates should have a strong expertise in path-planning and SLAM.
3. Information theory for real-time applications: Candidates should have a theoretical background in stochastic control, information theoretic security, or zero/finite-delay data compression and channel coding.

The intended start date is September 1, 2019, but the exact start date is negotiable. The appointment is for one year, with possible extension depending on research performance and availability of funds. The salary will be determined based on the postdoctoral salary rate of the University of Texas at Austin Cockrell School of Engineering.

Interested candidates are encouraged to email Dr. Takashi Tanaka with the following information: (i) a curriculum vitae; (ii) a research statement; and (ii) a list of three referees with contact information.

Our papers “Path-Integral Control for Optimal Vehicle Routing Over Nonlinear and Dynamic Traffic Systems” (Joint work with Dr. Y. Wasa and Dr. K. Uchida) and “Strategic Bidding of Private Information for Principal-Agent Type Dynamic LQ Networks” (Joint work with Dr. Y. Wasa Dr. T. Murao and Dr. K. Uchida) will be presented at ECC2019@ Napoli.

I gave a talk titled “Information Theory for Feedback Systems: Recent Developments and Perspectives” at 2018 Texas Systems Day at the University of Texas at Dallas.