PhD Position - Design of Experiment and Digital Process Optimization for CO2 Electrolysis

PhD Position - Design of Experiment and Digital Process Optimization for CO2 Electrolysis

At the Institute of Energy Technologies – Fundamentals of Electrochemistry (IET-1), we conduct research on cutting‑edge topics related to the energy transition and structural change. We work on the battery of the future, exploring innovative battery concepts, and investigate how carbon dioxide (CO₂) can be transformed from a climate killer into a raw material of the future. The goal is to create cost‑effective batteries, fuel cells and electrolysers with improved energy and power density, longer service life and maximum safety. Hydrogen production based on renewable energy sources via high‑ and low‑temperature electrolysis is of increasing importance to us.

Your Job

We study the electrolysis of CO₂ for the sustainable conversion of green energy into chemical energy carriers to defossilise the chemical industry. Process control and optimisation of electrolysis from the cell to the stack require automated monitoring, analysis and control of operating parameters and processes. High‑throughput experimental systems are enhanced using Design of Experiment (DoE) approaches to efficiently explore high‑dimensional parameter space. DoE is essential for data‑efficient exploration and optimisation of the process parameter space, and for adaptive, data‑driven machine‑learning approaches to map the electrolysis process to a digital twin. Parallelly, data workflows and system control interfaces (APIs) are being developed to automate process monitoring and control. API‑based integration of the digital twin into the process control of CO₂ electrolysis enables autonomous operation of the system, differentiating between market‑, system‑ or network‑based operating modes depending on requirements.

Your Project Tasks & Structure

• Development of APIs for electrolysis systems and analysis devices
• Implementation of autonomous process control
• Conceptualisation and implementation of degradation models for electrolysis
• Studying performance and degradation of electrolysis in dependence on different operating modes

Your Profile

• Excellent Master’s degree and subsequent PhD in chemical engineering, computational engineering, computational mathematics, data sciences / analysis, system or process engineering, or related fields
• Excellent knowledge of API programming and automation engineering such as LabVIEW and/or EPICS
• Comprehensive knowledge of data science, data analysis, data management and machine learning
• Advanced programming skills with Python
• Experience with data‑driven machine learning (SINDy, LASSO, SISSO packages)
• Basic knowledge of electrochemistry desirable
• Interest in interdisciplinary research projects and excellent cooperation and communication skills and team‑working skills
• Very good command of written and spoken English (at least B2 level according to the CEFR) and ideally business‑fluent German skills

Our Benefits for You

• Meaningful tasks: The position offers a varied and diverse role in an international environment
• Work‑life balance: Optimal conditions for balancing work and private life and a family‑friendly company policy. Flexible working (location) available after consultation
• Vacation: 30 days of vacation plus additional days off (e.g. between Christmas and New Year’s)
• Knowledge & further training: Targeted, individual support for professional development
• Health & well‑being: Comprehensive occupational health management program, beach volleyball court, running groups, yoga classes and more; company medical service and social counselling available
• Campus experience: Research campus in the countryside with collegial exchange and sporting activities on site; cafeteria with options and lake view lunch break
• Successful start: Structured training and support from the beginning with Welcome Days and Welcome Guide
• Fair remuneration: Classified in pay grade 13 of the TVöD‑Bund; salary information available on the BMI website
• Fixed‑term: Position initially 2 years, extendable until May 2029 if successful
• Support for international employees: International Advisory Service to assist with start‑up

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation/identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realise their potential is important to us.