Graduate Engineering Intern – Building Controls Modeling & Simulation

Daikin Applied Americas•Plymouth, MN

1d•Hybrid

About The Position

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.

Requirements

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 equation‑based 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, deliverables‑driven project and communicate results clearly.

Nice To Haves

Familiarity with the Berkeley National Laboratory Modelica Buildings Modeling Library.
Experience with the FMI/FMU standard and multi‑tool simulation workflows.
Knowledge of building automation systems (BAS) architectures and control strategies.
Exposure to MiL, SiL, real‑time, or accelerated simulation workflows.
Coursework or experience in optimization, reduced‑order modeling, or machine learning.
Interest in applying AI/ML methods to physics‑based engineering models and decision‑support tools.

Responsibilities

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 Mock‑up 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 faster‑than‑real‑time 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 design‑space exploration) to enhance scalability and usability.