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Model Predictive Control Phd Jobs (NOW HIRING)

Amazon

Applied Scientist, Navigation

San Francisco, CA · On-site

... model predictive control (MPC)-based trajectory planning. You will develop navigation solutions ... BASIC QUALIFICATIONS - Experience programming in Java, C++, Python or related language - PhD in ...

IntelliPro Group Inc.

Control engineer

Santa Clara, CA · On-site

$150K - $230K/yr

Master's or PhD in Robotics, Controls, Mechanical Engineering, or related technical field * 4+ ... Strong expertise in control theory including nonlinear control, model predictive control, and ...

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How much do model predictive control phd jobs pay per year?

As of Jun 3, 2026, the average yearly pay for model predictive control phd in the United States is $96,574.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $108,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Model Predictive Control (MPC) PhD, you need advanced knowledge in control theory, optimization, and applied mathematics, typically supported by a doctoral degree in engineering or a related field. Expertise in simulation software (like MATLAB/Simulink), programming languages (such as Python or C++), and familiarity with real-time control systems is essential. Strong analytical thinking, problem-solving, and effective communication skills help distinguish top candidates in both academic and industry settings. Mastery of these technical and soft skills ensures the ability to develop, implement, and communicate sophisticated control solutions for complex, real-world systems.

What are the typical research and collaboration opportunities available to a Model Predictive Control PhD candidate during their studies?

As a Model Predictive Control PhD candidate, you will often collaborate with multidisciplinary teams, including faculty advisors, postdoctoral researchers, and industry partners. Research opportunities may include working on advanced control algorithms for real-world systems such as robotics, automotive, or energy applications. Many programs encourage publishing in peer-reviewed journals and presenting at conferences, which helps build your professional network. Additionally, you may have the chance to contribute to grant-funded projects, mentor undergraduate students, and participate in workshops or internships related to control systems.

What is a Model Predictive Control (MPC) PhD?

A Model Predictive Control (MPC) PhD is a doctoral program focused on advanced research in model predictive control, a type of control algorithm extensively used in engineering, robotics, and process industries. Students in this program typically develop new theories, algorithms, or applications for MPC, contributing to areas such as optimization, real-time systems, or autonomous systems. The program involves substantial coursework, original research, and the completion of a dissertation under faculty supervision. Graduates are prepared for careers in academia, industry research, or advanced engineering roles.

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

AspectModel Predictive Control PhdControl Systems Engineer
Required CredentialsPhD in Control Engineering or related fieldBachelor's or Master's in Engineering or related field
Work EnvironmentResearch, academia, or R&D departmentsIndustry, manufacturing, or automation sectors
Industry UsageDeveloping advanced control algorithms, research projectsImplementing control solutions, system design

The Model Predictive Control Phd typically focuses on research and development of advanced control algorithms, often in academic or R&D settings, requiring a PhD. Control Systems Engineers work in industry, applying control principles to real-world systems, often with a bachelor's or master's degree. Both roles involve control theory but differ in scope, environment, and experience level.

Infographic showing various Model Predictive Control Phd job openings in the United States as of May 2026, with employment types broken down into 10% Internship, 85% Full Time, and 5% Contract. Highlights an 85% In-person, 5% Hybrid, and 10% Remote job distribution, with an average salary of $96,574 per year, or $46.4 per hour.

Humanoid Robotic Engineer

Hyphen Connect Limited

San Francisco, CA • On-site

Full-time

Posted 11 days ago

Job description

We are searching for a Humanoid Robotic Engineer specialized in bipedal locomotion and manipulation. This role involves designing and implementing sophisticated control algorithms, with a focus on Reinforcement Learning, to enhance the performance of robotic systems both in simulation and real-world applications.
Responsibilities:
  • Develop and implement advanced control algorithms for bipedal locomotion and manipulation.
  • Train Reinforcement Learning (RL) policies in simulation and deploy them on physical hardware.
  • Conduct sim-to-real transfer and analyze gait/kinematics telemetry.

Required Skills:
  • Strong background in Model Predictive Control (MPC) and Whole-Body Control.
  • Extensive experience with robotic simulation environments (e.g., NVIDIA Isaac Sim, MuJoCo).
  • Familiarity with modern RL frameworks.

Apply