Descripción
GENERAL MISSION Develop model-based design (MBD) control strategies for thermal system management. Optimize compressor capacity modulation, mode switching, and energy efficiency through advanced algorithms and real-time vehicle integration. KEY RESPONSIBILITIES ● Develop MATLAB/Simulink control system models and algorithms ● Design compressor capacity modulation strategy for optimal cooling/heating ● Optimize mode switching logic (cooling ↔ heating ↔ defrost) ● Energy management for electric vehicle (battery thermal conditioning) ● Model-in-the-loop (MIL) and hardware-in-the-loop (HIL) validation ● Integrate control algorithms with vehicle embedded software (CAN communication) ● Collaborate with test engineers for SIL/HIL validation ● Evaluate AI/ML approaches for intelligent thermal management (future roadmap) EDUCATION & QUALIFICATIONS Minimum: Master's degree in Control Engineering, Electrical Engineering, Mechanical Engineering, or related field University Quality: 8-10/10 (strong algorithmic and research background valued) Specialization: Control Systems, Model-Based Design, Embedded Systems, Machine Learning (optional) PhD advantageous: Demonstrates research depth and complex problem-solving capability EXPERIENCE REQUIREMENTS Years: 5-7+ years in automotive control systems or embedded software development Background: Minimum 2 major thermal/powertrain control projects with MATLAB/Simulink Technical Focus: Control algorithm development, SIL/HIL validation, vehicle-level software integration OEM or Tier1 Experience: Bosch, Continental, Marelli, VW/Audi electronics, or equivalent Core Competencies (Score Required): Technical Competency - Required - Evidence : Thermal Systems - 8/10 - Understanding of HVAC components, refrigerant cycles, thermal dynamics Vehicle Integration - 8/10 - CAN communication, OBD-II signals, embedded software interfaces AI/Data Science - 9/10 - Machine learning (Python/TensorFlow), data analysis, optimization algorithms Transversal Knowle