Research Scientist in AI/ML for Dynamics and Control (Hybrid)

RTX•East Hartford, CT

8d•Hybrid

About The Position

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. We are: Empowering innovation among the company’s businesses. Solving customers’ critical problems. Developing breakthroughs for a safer, more connected world. Working with major universities and national laboratories on groundbreaking research. The Dynamics, Controls, and Autonomy team supports dynamical system analysis and modeling, control system analysis and design, and autonomous systems research for all RTX business units, including both development of novel solutions for future products and solving the toughest problems with current products. In parallel, we are working with government customers on more broadly applicable technology.

Requirements

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 , optimization-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, Sim2Real 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 modeling 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 TensorFlow

Nice To Haves

Master degree in Mathematics, Physics, Computer Science or Engineering with minimum 5 years of full-time industrial experience .
Novel approaches for safety including Control Barrier Functions (CBF)
Hardware-in-the-Loop validation and real-time/embedded implementation of control laws
experience with Speedgoat, dSPACE, or LabView/NIDAQ
FPGA programming
C/C++ programming
Experience with multi-agent collaborative autonomy, including Multi-agent autonomous behaviors
Decentralized mission planning and execution
Hands-on experience with implementation of autonomy algorithms in high-fidelity simulations and/or hardware platforms: PX4 or ArduPilot 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 development
Resilient contingency management for multi-agent autonomous systems
Human-robot teaming
Application experience in one or more of the following: Manufacturing and inspection operations autonomous systems, including assurance for autonomy safety and certification in aerospace gas turbine engine modeling and control; Guidance, Navigation, and Control (aircraft, spacecraft, or missiles) hypersonic propulsion; electric or hybrid-electric propulsion for aircraft; control co-design
Experience with Large Language Models and agentic control
Experience with writing proposals for government-funded research, record of grants a record of innovation as evidenced by patent applications, a track record of writing proposals for government funded research programs, and/or high-quality journal and conference publications.
Active Security Clearance
Strong analytical, problem-solving and interpersonal skills with track record of teamwork, adaptability, innovation and initiative
Clear and effective communication with all levels of management, business development, researchers and customers
Ability to focus on results in a fast-paced, dynamic team environment
Ability to work independently with limited direction and in multidisciplinary environment to accomplish project goals
The preferred candidate will look at open-ended tough problems as an opportunity to innovate and develop novel solutions

Responsibilities

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 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, and peer-reviewed articles, and developing intellectual property