• Strong mathematical skills, including linear algebra, numerical methods, probability, and stochastic process

  • Design, development, and implementation of automation test software and procedure

  • Design, develop, maintain automated test equipment

  • Equipment qualification and validation testing of automated test stations

  • Verification and validation testing of computed tomography (CT) products

  • Software update, maintenance, and troubleshooting of automated test stations

  • Failure mode analysis of automated test stations based on test reports and taking appropriate actions

  • Software and hardware testing, troubleshooting, and debugging to find errors and update test procedure

  • Hands-on experience in testing and troubleshooting electrical systems including the use of lab tools

  • Hands-on experience in testing and troubleshooting electrical systems including the use of lab tools e.g., digital multimeters, oscilloscopes, etc.

  • Analytical knowledge in linear system design and control e.g., stability, controllability, observability, etc.

  • Analysis and design of feedback control systems and Lyapunov-based nonlinear system control

  • Analysis and design of PD and PID controller for linear system control

  • Developed algorithms for UAV swarm control and guidance using decision theoretic frameworks

  • Developed average consensus-based sensor fusion algorithm and compared to Kalman filter based sensor fusion as a benchmark

  • Familiar with core problems in robotics including state estimation (Kalman filter), SLAM, etc.

  • Research experience in autonomous systems, optimal control, swarm intelligence, and computer vision

  • Substantial knowledge in optimization techniques e.g., gradient descent, simulated annealing, particle swarm optimization, genetic algorithm, etc.

  • Knowledge in robot navigation and path planning algorithms e.g., A-star search, potential field, etc.

  • Hands on experience with Arduino programming

  • Hands-on experience in image processing and computer vision with OpenCV, Skimage, etc.

  • Familiar with computer vision sensors (LIDAR, Optical Camera)

  • Familiar with camera parameters, image formation, construction of projection matrix, and estimation of essential and fundamental matrix

  • Familiar with applied machine learning and deep learning techniques e.g., statistical pattern recognition, principal component analysis (PCA), regression, k-means clustering, CNN, Mask RCNN, YOLO, etc.

  • Hands-on experience in feature detection \& matching, 3D reconstruction, image segmentation, classification