Lead Researcher - Advanced Control Systems

Job Description Summary

GE Vernova’s Advanced Research business is the central innovation engine for GE’s portfolio of energy businesses, which is unified under one banner called GE Vernova. The research organization is made up of 300+ researchers representing virtually every major scientific and engineering discipline. Collectively, we drive major research programs and initiatives to decarbonize power, accelerate renewables, and promote electrification and the creation of a 21st century grid to power a zero-carbon energy future.

We are seeking a highly motivated and skilled researcher to join our team with a primary focus on advanced controls development for energy systems. As a Lead Researcher, you will spearhead the design and implementation of next-generation control architectures, provide technical direction and mentorship to a team of engineers and researchers, and drive innovation in model-based control, adaptive control, and real-time optimization to advance GE Vernova’s energy portfolio.

Job Description

Roles and responsibilities

Lead the design, development, and deployment of advanced control strategies including model based control (MBC), adaptive control, robust control, and nonlinear control for complex energy systems.

Architect control system frameworks for dynamic systems, ensuring stability, performance, scalability, and seamless integration with hardware and software platforms.

Lead system modelling, simulation, and hardware-in-the-loop (HIL) testing to validate and refine control algorithms before field deployment.

Drive the application of signal processing techniques for sensor data conditioning, state estimation, and fault detection to support control system performance.

Direct the analysis of complex operational datasets to inform control design decisions and guide the development of data-driven control and optimization algorithms.

Lead the integration of machine learning and AI techniques with classical control methods to develop intelligent, self-tuning control systems.

Oversee the development of predictive models, digital twins, and diagnostics tools to enhance system reliability and control performance.

Lead project planning, manage timelines, deliverables, and technical risks, ensuring alignment with business goals and stakeholder expectations.

Champion innovation by staying at the forefront of advancements in control theory, real-time optimization, and AI/ML for controls, and foster a culture of continuous learning through literature review, conference participation, and internal knowledge sharing.

Required Qualifications

PhD from a reputed institution with 5+ years’ experience, or a master's degree with 8+ years’ experience in control systems engineering, controls development, or a closely related discipline.

Deep expertise in advanced control theory and design, including model predictive control, adaptive control, robust control, state estimation (e.g., Kalman filtering, extended Kalman filters, unscented Kalman filters), and real-time optimization, with demonstrated ability to apply them to industrial-scale systems.

Strong understanding of signal processing fundamentals and their application to control systems, including filtering, spectral analysis, and sensor fusion techniques such as Kalman-based estimation methods.

Proficiency in programming languages and tools commonly used in controls development, such as MATLAB/Simulink, Python, and C/C++, with experience in rapid prototyping and code generation for embedded controllers.

Experience with simulation environments, hardware-in-the-loop testing, and real-time control platforms. Familiarity with software development best practices, including version control (e.g., Git), agile methodologies, and code review processes.

Excellent problem-solving skills and ability to think critically and creatively. Proven leadership and communication skills with the ability to influence cross-functional teams and present to senior stakeholders.

Demonstrated ability to build prototypes and quickly develop proof of concepts to show solution feasibility, with a track record of translating control algorithms from research into production-ready deployments.

Additional Information