admin | 2026-03-13 | 
Location: Düsseldorf, Germany (On-site or hybrid)
Duration: 6-9 months (flexible start date)
Supervision: Dr. Yash Raka
Application Deadline: March 31, 2026
Position: Internship (career-track with possibility of full-time employment)
Project Overview
We are seeking a highly skilled candidate with experience/expertise in Machine Learning to drive the development and application of advanced ML models for electrochemical energy systems, specifically Fuel Cells and Electrolysers.
This role is pivotal in establishing rigorous ML techniques for Reduced Order Modelling (ROM), advanced control strategies, and predictive maintenance. You will leverage large experimental datasets (from simulation, lab, and field operations) or generate synthetic data to create data-driven digital twins that enable faster design cycles, optimised performance, and increased system longevity.
Key Responsibilities
- ML Model Development: Design, implement, and validate high-fidelity machine learning models (e.g., physics-informed ML, advanced statistical modelling) for fuel cell and electrolyser systems.
- Data and Feature Engineering: Conduct rigorous Exploratory Data Analysis (EDA), feature engineering, and data preprocessing on complex time-series data from energy systems.
- MLOps and Pipelines: Integrate models into robust, reproducible ML pipelines using MLOps tools and standardise deployment processes.
- Collaboration: Work closely with electrochemical, chemical, and process engineers to understand physical constraints and integrate ML models into overall system design.
- Documentation & Dissemination: Maintain excellent documentation, track experiments effectively, and contribute to technical reports and scientific publications.
Required Education & Skills
- Education: M.S. or PhD in Machine Learning, Computer Science, Data Science, Chemical/Process Engineering, Physics, or related field.
- Programming: Expert proficiency in Python with core ML/DL frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, Pandas).
- Technical Expertise: Experience with time-series analysis, ROM, and physical system modelling. Familiarity with electrochemical systems (fuel cells, batteries, electrolysers) is a strong plus but not a requirement.
- Software Engineering: Familiarity with MLOps concepts, version control (Git), and deployment tools.
- Personal Skills: A self-starter with the ability to work independently and communicate technical concepts effectively.
How to Apply
Please apply using the form below or send the following documents to info@hdhyundai-erc.com:
- CV or resume
- Short cover letter detailing your specific experience in ML
- GitHub or portfolio demonstrating relevant projects (if available)
Hiring Process
- Introductory Call — Candidate presentation and overview of HD Hyundai Europe R&D Center
- Technical Assessment — Short technical task followed by presentation
- Final Interview — Interview and offer discussion