Physics Informed Machine Learning Jobs in Minnesota

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Graduate Engineering Intern - Building Controls Modeling & Simulation - Plymouth, MN - Hybrid

Make your mark at the world's largest HVAC company

Daikin Applied is seeking a highly motivated graduate engineering student for a full-time, 10 month engagement as a Graduate Engineering Intern (Building Controls Modeling & Simulation). This position will support the development of a generic, parameterizable building energy and controls model using Modelica / Dymola, with integration into FMU-based workflows and MATLAB/Simulink environments.

This effort will serve as a proof of concept (PoC) demonstrating that physics-based building models can be used to evaluate transient behavior, robustness, and performance of building automation and control strategies in greater than real time Model in the Loop (MiL) simulations.

The longer-term vision is to evolve this capability into a scalable engineering and decision support tool, potentially leveraging AI and machine learning, to help applications and sales engineers size, tune, and compare optimal building control solutions across a wide range of building types and operating conditions.

Come be a part of an exciting journey at Daikin Applied, where innovation and excellence drive our every endeavor!

Location:Plymouth, MN - Hybrid

Timeline:This Internship opportunity will start approximately May-June 2026 (flexible to align with academic schedules) and has an end date of December 2026

What you will do:

• Develop a generic, modular, and parameterizable building model in Modelica/Dymola suitable for multiple building archetypes and HVAC/control configurations.
• Leverage and extend existing Modelica building libraries (e.g., Berkeley National Laboratory / Modelica Buildings Library) where appropriate.
• Implement and evaluate building automation and control strategies within a dynamic simulation framework.
• Export and integrate models as FMUs (Functional Mockup Units) for use in MATLAB/Simulink MiL simulations.
• Demonstrate transient system behavior, control robustness, and performance under disturbances, parameter variation, and uncertainty.
• Optimize model structure and solver settings to enable fasterthanrealtime simulation performance.
• Collaborate with engineering stakeholders to define modeling scope, use cases, and success criteria for the PoC.
• Clearly document model architecture, assumptions, limitations, and validation results.
• Explore opportunities to incorporate AI/ML techniques (e.g., surrogate modeling, optimization, or designspace exploration) to enhance scalability and usability.

What's in it for you:

• The ability to make an impact and shape your career with a company that is passionate about growth
• The support of an organization that believes it is vital to include and engage diverse people, perspectives, and ideas to achieve our best

Minimum Qualifications:

• Currently enrolled M.S. or Ph.D. student in Mechanical Engineering, Electrical Engineering, Architectural Engineering, Controls Engineering, or a closely related field.
• Academic or research focus in building automation, building energy modeling, HVAC systems, or advanced control methods.
• Experience with Modelica and Dymola, or equivalent equationbased physical modeling tools.
• Strong foundation in dynamic systems, control theory, and numerical simulation.
• Experience using MATLAB/Simulink for modeling, simulation, or control development.
• Proficiency in technical programming or scripting (MATLAB, Python, or similar).
• Ability to work independently on an applied, deliverablesdriven project and communicate results clearly.

Preferred Qualifications:

• Familiarity with the Berkeley National Laboratory Modelica Buildings Modeling Library.
• Experience with the FMI/FMU standard and multitool simulation workflows.
• Knowledge of building automation systems (BAS) architectures and control strategies.
• Exposure to MiL, SiL, realtime, or accelerated simulation workflows.
• Coursework or experience in optimization, reducedorder modeling, or machine learning.
• Interest in applying AI/ML methods to physicsbased engineering models and decisionsupport tools.

Benefits:

• Daikin Applied offers the following benefits for this position, subject to applicable eligibility requirements:
• Multiple medical insurance plan options + dental and vision insurance
• 401K retirement plan with employer match
• Paid time off and company paid holidays
• Paid sick time in accordance with the federal, state and local law
• Tuition Reimbursement after 6 months of continuous service

The typical hourly rate for this position ranges from$30.00/ hr. to $33.00 / hr. in Minnesota. The range displayedrepresentsthe pay range for all positions in the job grade which this position falls. Individual base pay will depend on a wide range of factors including your skills, qualifications, experience, and location.

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