Control Systems & Modeling Engineer, R&D

About the role

ON.energy is seeking a Control Systems & Modeling Engineer to join its R&D Modeling and Control Strategies team. The role focuses on developing advanced control strategies and mathematical models for non‑linear dynamic systems that support power electronics and renewable energy solutions.

Key responsibilities

• Design, simulate, and implement advanced control strategies and mathematical models for non‑linear dynamic systems.
• Work at the intersection of power electronics and renewable energy systems, including inverters, microgrids, and battery energy storage systems (BESS).
• Apply system identification techniques for controller optimization and parameter estimation.
• Participate in simulation, prototyping, and real‑time testing phases in internal and external laboratories.
• Collaborate closely with a multidisciplinary technical and academic team.

Required profile

• B.S. or M.S. in Electronics Engineering, Electrical Engineering, or related fields (mandatory).
• Current student or graduate of a Master’s program in Automation and Control or similar.
• Strong foundation in power electronics, power systems dynamics, and mathematical modeling (state‑space, system identification).
• Proficiency in MATLAB/Simulink and Python applied to control systems.
• Professional fluency in English (technical reading and communication).
• Preferably residing in Buenos Aires; willingness to travel for meetings and laboratory work.
• Knowledge or experience with machine‑learning techniques for control optimization.
• Familiarity with simulation tools such as PSCAD, LabVIEW, and PSIM.
• Experience or knowledge of validation methodologies: MIL, SIL, PHIL, CHIL, Alpha Testing.
• Experience with Rapid‑Control‑Prototyping (RCP) techniques.

Required skills

• MATLAB
• Simulink
• Python
• Power electronics
• Inverters
• Microgrids
• Battery Energy Storage Systems (BESS)
• System identification
• Machine learning for control
• PSCAD
• LabVIEW
• PSIM
• MIL, SIL, PHIL, CHIL testing
• Alpha testing
• Rapid‑Control‑Prototyping