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Model Predictive Control Jobs in Connecticut (NOW HIRING)

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Model Predictive Control information

What is Model Predictive Control?

Model Predictive Control (MPC) is an advanced method of process control that uses a mathematical model to predict and optimize the future behavior of a system. It works by solving an optimization problem at each control step to determine the best sequence of control actions, taking into account system constraints and objectives. MPC is widely used in industries such as chemical processing, energy, and automotive because it can handle multivariable control problems and anticipate future events. Its predictive nature allows for improved performance, stability, and efficiency compared to traditional control methods.

What is the difference between Model Predictive Control vs Control Systems Engineer?

AspectModel Predictive ControlControl Systems Engineer
CredentialsEngineering degree, control theory, process modelingEngineering degree, control systems, automation
Work EnvironmentIndustrial automation, process control, manufacturingDesign, develop, and maintain control systems across industries
Industry UsageProcess industries, chemical, oil & gas, manufacturingAutomation, robotics, embedded systems, industrial sectors

Model Predictive Control (MPC) focuses on advanced control algorithms for optimizing processes, while Control Systems Engineers design and implement various control systems. MPC is a specialized skill within control engineering, often requiring knowledge of process modeling and optimization, whereas Control Systems Engineers have broader responsibilities across multiple control technologies. Both roles are essential in industrial automation but differ in scope and application.

What are the typical challenges faced by engineers working with Model Predictive Control (MPC) systems in an industrial setting?

Engineers working with Model Predictive Control systems often encounter challenges related to model accuracy, computational demands, and real-time implementation. Ensuring the process model accurately represents the plant dynamics is critical, as discrepancies can lead to suboptimal control performance. Additionally, MPC algorithms can be computationally intensive, particularly for large-scale or fast processes, requiring careful tuning and optimization to maintain real-time operation. Collaboration with process engineers and IT specialists is common, as integrating MPC with existing control systems and plant infrastructure is a key part of the role.

What are the key skills and qualifications needed to thrive as a Model Predictive Control (MPC) Engineer, and why are they important?

To thrive as a Model Predictive Control Engineer, you need strong foundations in control theory, applied mathematics, and process engineering, usually supported by a degree in engineering or a related field. Proficiency with simulation tools such as MATLAB/Simulink, programming languages like Python or C++, and familiarity with industrial automation systems are typically required. Analytical thinking, problem-solving abilities, and effective communication skills help distinguish top performers in this role. These skills are essential for designing, implementing, and optimizing advanced control algorithms that improve system performance and reliability in complex industrial environments.
What are popular job titles related to Model Predictive Control jobs in Connecticut? For Model Predictive Control jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Connecticut look for? The top searched job categories for Model Predictive Control jobs in Connecticut are:
Infographic showing various Model Predictive Control job openings in Connecticut as of July 2026, with employment types broken down into 1% As Needed, 79% Full Time, 16% Part Time, 1% Temporary, 2% Contract, and 1% Nights. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution.
Research Scientist in AI/ML for Dynamics and Control (Hybrid)

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

RTX Corporate

East Hartford, CT • Hybrid

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 3 days ago


RTX rating

8.2

Company rating: 8.2 out of 10

Based on 85 frontline employees who took The Breakroom Quiz

31st of 63 rated aerospace companies


Job description

Date Posted:

2026-06-18

Country:

United States of America

Location:

US-CT-EAST HARTFORD-RTRC L ~ 411 Silver Ln ~ RTRC L

Position Role Type:

Hybrid

U.S. Citizen, U.S. Person, or Immigration Status Requirements:

U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Security Clearance Type:

None/Not Required

Security Clearance Status:

Not Required

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.

What You Will Do

  • 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.

What You Will Learn

  • 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

Qualifications You Must Have

  • 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

Qualifications We Prefer

  • 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

Additional Skills and Abilities
  • 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

Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as:

Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.

Candidates will learn more about role type and current site status throughout the recruiting process. For onsite and hybrid roles, commuting to and from the assigned site is the employee’s personal responsibility.

As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote.

The salary range for this role is 86,800 USD - 165,200 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company’s performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.

RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act.

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