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Model Predictive Control Jobs in Washington (NOW HIRING)

KBR

Modeler III

Arlington, VA · On-site

$63 - $81.75/hr

Applies broad understanding of military command & control and force structure at the unified ... Model (STORM) and/or Advanced Framework for Simulation, Integration, and Modeling (AFSIM) * Eight ...

KBR, Inc.

Modeler III

Arlington, VA · On-site

$63 - $81.75/hr

Applies broad understanding of military command & control and force structure at the unified ... Model (STORM) and/or Advanced Framework for Simulation, Integration, and Modeling (AFSIM) * Eight ...

Redhorse Corporation

Data Scientist

Arlington, VA · On-site +1

... first line quality control check for data cleaning, WUC analyses, NLP models and resulting ... Sustain existing WUC predictive failure models * Support selection and analysis of new WUC ...

Seekr

Senior AI Solutions Engineer

Reston, VA · On-site

$57.50 - $74/hr

Experience training and employing predictive AI models in a government context * Solid knowledge of ... control mechanisms, and Layer 2+ analysis and exploitation

Everwatch

Systems Engineer, Senior

Annapolis Junction, MD · On-site

$106K - $146K/yr

Harnessing the most advanced technology and solutions, we strengthen defenses and control ... modeling, predictive analytics, or decision-support systems * Knowledge of data science, machine ...

Seekr

Senior AI Solutions Engineer

Reston, VA · On-site

$57.50 - $74/hr

Experience training and employing predictive AI models in a government context * Solid knowledge of ... common control mechanisms, and Layer 2+ analysis and exploitation About the Company: Seekr is a ...

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All JobsModel Predictive Control JobsModel Predictive Control Jobs in Washington

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 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 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 Washington? For Model Predictive Control jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Washington look for? The top searched job categories for Model Predictive Control jobs in Washington are:
Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Washington as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 19% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution.

NIST PREP Postdoc Associate in Metrology of Materials, Surfaces, and Processes for Semiconductor Adv

Southeastern Universities Research Association

Gaithersburg, MD • On-site

$70K - $90K/yr

Full-time

Re-posted 6 days ago

Job description

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest and thus requires that such institutions be the recipients of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
Research Title: Metrology of Materials, Surfaces, and Processes for Semiconductor Advanced Packaging
The work will entail: We are seeking a highly motivated researcher to advance measurement science for next-generation hybrid advanced packaging. This role will contribute to the development of novel surface and materials metrology methods that enable predictive control of bonding processes and heterogeneous integration. The successful candidate will work within the Dynamic Mechanical Metrology Project of the Quantum Measurement Division to help establish the quantitative foundations needed for reliable, high-density microelectronic assembly, supporting national efforts to strengthen U.S. leadership in semiconductor manufacturing and advanced packaging technologies.
The main responsibilities are:
  • Design, model, and performance of test methods to evaluate bond quality of bonded chip pairs using different wafer materials and bond methods.
  • Design and fabrication of test rigs for bond strength measurement.
  • Implement bond strength testing at cryogenic and high temperatures.
  • Closely coordinate with teams performing materials characterization, surface and thin film thin film characterization.

Qualifications
Necessary Qualifications:
  • PhD in mechanical engineer, physics, electrical engineering, materials science, or a related field.
  • Experience with wafer and/or hybrid chip bonding processes.
  • Experience with destructive test methods, such as those utilizing materials test machines or related instruments.
  • Proficiency in finite element modeling, using tools such as COMSOL or ANSYS
  • Proficiency in CAD, using tools such as SolidWorks or Autodesk.
  • Proficiency in programming languages, such as Python, Java, or Matlab.
  • Excellent communication skills and ability to work effectively in a team.

Desirable Qualifications:
  • Experience, including process development, in back-end semiconductor device fabrication, including wafer cleaning and handling.
  • Familiarity with silicon electronic and photonic device processing
  • Experience with custom infrared microscopy and optical measurement setups.
  • US citizenship strongly preferred

Privacy Act StatementAuthority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate the administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated. By applying to a CHIPS-funded PREP opportunity, you also acknowledge that participation in the project requires signing a Non-Disclosure Agreement (NDA) prior to beginning any work.
SURA is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
PREP0004280

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