Research Scientist in AI​/ML Dynamics and Control; Hybrid

Overview

At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market‑leading businesses, world‑class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world.

Join us and help shape the future of aerospace and defense. The Dynamics, Control, and Autonomy Team, part of the Intelligent & Cyber‑Physical Systems Department at RTX Technology Research Center (RTRC) is looking for a highly motivated individual for the position of research engineer specialized in Learning for Dynamics and Control. RTRC serves as the innovation hub for RTX. We conduct basic and applied research in a stimulating multi‑disciplinary environment where scientists, engineers, practitioners and subject matter experts collaborate and exchange experience.

We transform that research into the solutions and products that help our businesses shape the future.

• Design and develop novel control solutions for aerospace and defense applications including, but not limited to, jet engines, missiles, autonomous vehicles and systems, avionics, aircraft power systems and air management, hypersonic vehicles, advanced manufacturing, and space systems.
• Work in a multidisciplinary setting, bringing a system‑level perspective to new cutting‑edge technologies from multiple fields (autonomy, power systems, cyber security, mechanical systems, aerodynamics, thermal management).
• Lead and support externally and internally sponsored programs, write external and internal research proposals.
• Disseminate research results through reports, conference proceedings, peer‑reviewed articles, and develop intellectual property.

• How to transition novel concepts from early technology stages to a state that impacts and influences our products, which in turn have global impact on society.
• How to build relationships both within our company and externally with industry, academia, and government agencies for long‑term impact.

• Ph.D. in Mathematics, Physics, Computer Science or Engineering.
• Strong fundamentals in control: standard multivariable control and estimation techniques (e.g., LQR/LQG, Kalman filters, optimisation‑based control, including Model Predictive Control), from formulating the problem to implementation in software.
• Experience with machine learning for control, including Reinforcement Learning (RL) for safety‑critical systems (e.g., model‑based RL, Sim2
  
  Real transfer learning, or safety guarantees using Control Barrier Functions).
• Verification & Validation of AI/ML control laws.
• Neural‑network representations of controllers and estimators (e.g., Physics‑Informed Neural Networks for MPC, or Neural Network based MPC).
• Control‑oriented modelling of physical systems, both from first principles and data‑driven (including learning‑based methods such as Physics‑Informed Neural Networks).
• Proficiency in MATLAB/Simulink, Python, PyTorch or Tensor Flow.

• Master’s degree in Mathematics, Physics, Computer Science or Engineering with a minimum of 5 years of full‑time industrial experience.
• Novel approaches for safety including Control Barrier Functions.
• Hardware‑in‑the‑Loop validation and real‑time/embedded implementation of control laws, experience with Speedgoat, dSPACE, or Lab View/NIDAQFPGA programming.
• C/C++ programming skill.
• Experience with multi‑agent collaborative autonomy, including multi‑agent autonomous behaviours.
• Decentralized mission planning and execution.
• Hands‑on experience with implementation of autonomy algorithms in high‑fidelity simulations and/or hardware platforms: PX4 or Ardu Pilot autopilots and software‑in‑the‑loop simulations.
• Robot Operating System (ROS/ROS2) and Gazebo simulation.
• Open‑source planning and perception software packages.
• Commercial UAV and UGV platforms.
• Experience with one or more of the following technical areas:
  • Neural and symbolic AI approaches for course of action…