Postdoctoral Research Associate, Electrical and Computer Engineering

We invite qualified candidates to join our group and participate in cutting-edge research. The chosen candidate will advance research at the intersection of robotics, control & prototyping, and AI/ML. The Postdoc will design, implement, and evaluate robotic systems - including real-time control stacks - and develop learning-enabled perception, planning, and vision-language model (VLM) pipelines. The role includes disseminating results via publications, patents, demos, and grants, in addition to mentoring students and contributing to course modules/workshops in robotics and related topics.

Outstanding UA benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; access to UA recreation and cultural activities; and more!

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• Lead, design, build and test cycles for robotic platforms and experimental rigs; develop real-time control (e.g., model-based, optimal, learning-augmented control), perception, and planning modules; run structured experiments and benchmarking.
• Architect high-quality research codebases in C++/Python/C# (e.g., ROS/ROS 2, RT frameworks, Unity/Unreal integration as needed); implement data pipelines, simulation, and CI/testing; maintain reproductible artifacts and documentation.
• Mentor undergraduate and graduate students; develop short course modules and/or run workshops in Robotics/AI/Control; support inclusive team culture and lab best practices.
• Lead and co-author journal/conference
  papers; prepare manuscripts for publication in peer-reviewed journals; create
  compelling presentations/demos and contribute to IP (invention disclosures and
  patents); present research at national and international conferences.
• Contribute to proposal
  development and grant writing (including preliminary data, methods, budgets
  text); prepare progress reports, and coordination with internal/external
  collaborators; interface with sponsors where applicable.
• Foster collaborations
  within the Department, with other units across the University, and with team
  members at other institutes.
• Participate and
  contribute to meetings with industry, academia and sponsors.
• Additional duties as assigned.

Knowledge, Skills, and Abilities

• Strong analysis
  skills, research, and technical writing skills.
• Ability to communicate
  professionally in a clear, concise manner orally and in writing.
• Knowledge of prototyping, specifically control (PID, MPC, optimal/robust, learning-augmented); perception &
  planning; sensor fusion; calibration; system identification.
• Programming skills, including C++,
  Python, C#; ROS/ROS 2; Git; Linux; build systems (CMake);
  real-time/latency-aware coding; simulation (Gazebo/Isaac/Unity/Unreal as
  applicable).
• Knowledge of experimental design, statistics, ablation studies, replicable pipelines,
  technical writing.
• Ability to create clear presentations and use strong interpersonal skills with a collaborative
  mindset; effective mentoring.

• PhD in Robotics, Electrical & Computer Engineering, Mechanical Engineering, Biomedical Engineering, Computer Science, or closely related field.
• Must have PhD conferred upon hire.

• Hands-on experience integrating hardware + software for robots (arms, mobile, mechatronics) and real-time control.
  
• Experience in computer vision, multimodal perception, or foundation models (VLMs/LLMs) applied to robotics.
  
• Track record of patent contributions and/or technology transfer.
  
• Experience preparing grants (NSF/NIH/DoD/industry) and coordinating collaborative deliverables.
  
• Prior experience mentoring/teaching, curriculum or workshop development.
  
• Experience with safety standards for robotics labs and human-robot interaction studies.
  
• Experience with CUDA/accelerators; optimization; SLAM; tactile/force control; AR/XR interfaces; Unity/C# for robotics visualization; DevOps/containers.
• Experience with AI/ML: Deep learning for vision/perception; VLMs and LLM tooling; dataset curation; evaluation/benchmarks; basic MLOps.

eazimi@arizona.edu