Key responsibilities will include but are not limited to:

• Conducting state-of-the-art measurement science research in robotics, advanced autonomy, and artificial intelligence systems.
• Utilizing deep learning, large language models (LLMs), reinforcement learning, and unsupervised machine learning techniques to enhance robot capabilities, human objective prediction, and decision-making architectures.
• Developing verification methods, transparent evaluation frameworks, and performance metrics to ensure robotic AI systems and automated sorting or ranking methodologies are inspectable and risk-aware.
• Implementing advanced filtering techniques and symbolic spatial relation models to optimize tracking, navigation, and behavioral classification in highly dynamic environments.
• Programming, simulating, and validating physical and simulated autonomous systems using a variety of modern software, libraries, and probabilistic frameworks.
• Helping develop standards, benchmark scenarios, and performance metrics for human-robot interaction (HRI), autonomous systems, and cooperative robotics integrated into complex environments.
• Developing test apparatuses, digital twins, and virtual/physical testbeds using 3D rendering, CAD, and sensor fusion tools to validate repeatability and reproducibility.
• Collaborating with interdisciplinary teams to design and optimize systems while publishing peer-reviewed research results in high-impact journals and international conferences.

Qualifications

• Ph.D. degree in Computer Science, Information and Computer Science, Modeling and Simulation Engineering, Aerospace Engineering, or a closely related quantitative field.

• Experience in: Deep learning models, large language models (LLMs), unsupervised clustering, autoencoders, probabilistic programming, AI planning tools, human-robot interaction, activity/intent recognition, path planning algorithms, state estimation, algorithmic auditing, explainable AI frameworks, usability studies, human-in-the-loop validation, risk-aware system design, 3D rendering, discrete event simulation, motion capture data integration, and inertial navigation system architectures.

• Proficiency in: Python, C/C++, Java, MATLAB, exposure to functional or specialized languages (e.g., Common Lisp), PyTorch, TensorFlow, ROS, OpenGL, probabilistic graphical libraries, LaTeX, 3D CAD modeling, 3D Printing workflow management, and modern developer tooling.