Researcher: Uncertainty Quantification & Surrogate Modelling

Location: bengaluru, in

Company Description

Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

Job Description

Roles & Responsibilities :

• The focus of this role is set on Uncertainty Quantification (UQ) for model-based system design

• You’ll have to model parameter uncertainties in order to guarantee best performance under all operating conditions for modern control system applications on distributed compute architectures.

• You’ll have to analyze and enrich complex system models using uncertainty quantification techniques, AI/ML and surrogate modelling methods and implement everything via the necessary scripting and programming.

• You’ll enable Bosch products to leverage new distributed deployments while meeting the requirements.

• You’ll be working in international, multi-disciplinary teams and perform necessary tasks to accomplish the project goals.

• You’ll publish research findings in reputable journals and conferences.

Qualifications

Educational qualification:

PhD from top Indian institutes (IITs, IIITs, IISc etc.) or from top international institutes with good track record of research publications related to:

- Applied Mathematics, Statistics, Computational Engg, Aerospace Engg, Computer Science or any other branch where the candidate has deep expertise in Uncertainty Quantification, Applied Mathematics and AI/ML techniques.

Mandatory/requires Skills :

• Excellent programming skills in python, C, C++

• Excellent knowledge of uncertainty quantification

• Expertise in AI such as machine learning / tabular foundation models / deep learning

• Expertise in system identification

• Sound fundamentals of dynamic system

• Sound fundamentals of ODEs, PDEs and numerical methods for solving them for various engineering problems

• Sound fundamentals of Linear algebra

• Excellent presentation and communication skills

Preferred Skills :
 

• Working knowledge on control systems and engineering will be an added advantage

• Experience in modern distributed systems architectures e.g. cloud/edge/E/E-architecture will be an added advantage

• Experience with simulation tools: MATLAB, Simulink and AMESim will be added advantage.

• Experience in agile working environment preferably Scrum Master certification will be an added advantage