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Participants will gain the capability to implement functional safety and cybersecurity processes across automotive product development, conduct hazard and risk analysis, integrate cybersecurity requirements into system design, and manage safety compliance across the vehicle lifecycle. They will also learn to coordinate safety and cybersecurity validation activities aligned with international automotive standards.
Data Analytics for Engineering in Automotive
Participants will be able to analyze multi-source vehicle data, apply reliability and predictive analytics models, interpret telematics-driven performance signals, reduce warranty risk, and support data-driven engineering decisions across the vehicle life cycle.
Participants will be able to explain electric powertrain architecture, design key subsystems including battery, motor, and inverter, apply performance calculations, develop validation plans, interpret test data, and ensure compliance with automotive performance and safety standards.
Unit Testing with LDRA for Automotive Software Quality
Participants will be able to implement structured unit testing strategies using LDRA, achieve required structural coverage levels, enforce coding standards compliance, generate audit-ready reports, and integrate unit testing into continuous integration pipelines. They will strengthen their ability to reduce defect leakage and improve software reliability in safety-critical automotive systems.
Participants will be able to differentiate AUTOSAR Classic, AUTOSAR Adaptive, and non-AUTOSAR embedded frameworks, design modular ECU architectures, manage software configuration and integration, and make informed framework selection decisions aligned to vehicle program requirements. They will strengthen their capability to improve software scalability, reusability, and long-term platform strategy.
Advanced Motor Control Systems for Automotive R&D
Participants will be able to develop and implement advanced motor control algorithms, optimize torque and efficiency across dynamic load conditions, validate systems using HIL/SIL frameworks, and troubleshoot real-world deployment issues in electric powertrain applications. They will strengthen their capability to translate system requirements into stable and scalable production-ready control architectures.
Hardware-in-the-Loop Testing for Automotive R&D
Participants will be able to plan and implement effective HIL testing strategies, configure test environments, simulate real-world operating and fault conditions, and analyze results to support design decisions. They will strengthen their ability to detect control, integration, and safety issues early in development, reducing downstream validation and launch risks.
Participants will be able to perform system-level failure analysis, construct and interpret fault trees, identify critical failure paths, and use FTA outputs to strengthen automotive design robustness, safety assurance, and validation strategies.
Dependent Failure Analysis for Automotive R&D
Participants will be able to identify dependent, common-cause, and cascading failures, evaluate interference and independence within automotive systems, and apply DFA techniques during design and validation to reduce system-level risks and improve overall vehicle reliability.
Software Measurement and Estimation for Automotive R&D
Participants will develop the ability to measure software size using structured methodologies, estimate development effort and cost, and support accurate project planning in automotive research and development. They will learn how to apply function point concepts, evaluate software requirements, and improve estimation reliability across software development projects.