Research Engineer: AI/ML enhanced control engineering
This research engineer role at Bosch focuses on integrating AI and control theory to develop adaptive, uncertainty-aware dynamic systems for real-world automotive applications. You will model, simulate, and secure learning-based control solutions for constrained linear, nonlinear, and distributed systems.
Skills required
About this role
Roles & Responsibilities :
As part of an interdisciplinary research team you will combine methods from artificial intelligence, control theory and computer science with the product portfolio of Bosch. Future dynamics systems need to adapt themselves and handle uncertainties during operation while safeguarding functionality. Join us in transforming complex challenges into real-world impact.
• Addressing technical challenges: You will analyze, model, and simulate dynamic and uncertain systems and develop secure approaches for solving complex learning and control engineering problems.
• Systematically develop solutions: You will use data-based methods to improve model-based control concepts and apply them to constrained linear, nonlinear, and distributed systems with parameter and structural uncertainties.
• Support Transfer: You maintain close contact with the customer, take into account product-specific requirements, and prepare solution approaches and minimum viable software products in a target-group-oriented manner.
• Bring new ideas into life: You continuously monitor and evaluate academic and industrial research and adapt new approaches to Bosch products.
Qualifications
Educational qualification:
MS/MTech, PhD from top Indian institutes (IITs, IISc etc.) in the field of Electronics and Communication Engg, Electrical Engg, Mechanical Engg, Mechatronics Engg, Cybernetics, Aerospace Engg, Computer Science Engg, from top international institutes with good academic track record.
Experience :
3+
Mandatory/requires Skills :
Sound knowledge of modern control theory is a must, with profound knowledge in concepts of Lyapunov stability analysis, optimal control (LQR/LQG), adaptive control, and robust control which are essential for handling unpredictable environments.
Sound knowledge and hands on experience in AI/ML methods and tools: Deep learning, GPRs, Bagging & boosting methods, Surrogate modelling techniques, Reduced order models.
Excellent programming skills in python.
Experience with simulation tools: MATLAB, Simulink
Sound understanding of theory of ODEs and system of ODEs and numerical methods to solve them.
Adept at systems thinking, with a focus on holistic problem-solving and recognizing patterns in complex environments
Excellent presentation and communication skills.
Preferred Skills :
Experience in C/C++ will be added advantage.
Hands on knowledge in tabular foundational models, transformer architectures will be an added advantage
knowledge of Linear algebra & Matrix theory.
Familiarity with high performance computing will be added advantage.
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