Hydrogen economy promises a source of clean energy, but it requires addressing critical issues associated with the assessment of hydrogen before being end-used. An issue associated is hydrogen embrittlement (HE). HE can cause catastrophic failures, especially in HE susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl. electrochemical charging. This includes supporting and setting up new HE testing facilities. This is part of a third-party funded project.
We are looking for a highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and code implementation in close cooperation with scientists. The position is limited to 3 years.
Equal opportunity is an important part of our personnel policy. We would therefore strongly encourage qualified women to apply for the position.
Your tasks
- develop surrogate models to approximate high-fidelity phase field simulations, incorporating physics-informed loss functions to enhance model accuracy and generalizability
- modify and extend existing phase field modeling frameworks to capture failure mechanisms associated with hydrogen embrittlement
- generate and curate datasets for training ML (e.g. DeepONet) models by materials testing to identify relevant parameters and ensure consistency between simulations and empirical observations
- dissemination of results by publications in peer-reviewed journals and presentation at consortia meetings, national and international conferences, and workshops
Your profile
Essential qualifications:
- MSc degree in materials science, mechanical engineering, physics or similar
- basic programming skills in one or more languages (Python, C/C++, or others)
- experience in mechanical testing
- profound knowledge of machine learning methods (e.g., neural networks, Gaussian processes, active learning)
- interest in materials science (e.g., SCC)
- excellent knowledge of English (written and spoken)
- high degree of motivation, creativity, and flexibility
- ability to work in an interdisciplinary and international team of scientists
Desirable qualifications:
- experience in processing experimental data
- experience with SCC
- international research experience
- willingness to learn German
We offer you
- an exciting and varied job in a research centre with around 1,000 employees from more than 60 nations
- a well-connected research campus (public transport bus) and best networking opportunities
- individual opportunities for further training
- social benefits according to the collective agreement of the public service and remuneration up to pay group 13 according to TV EntgO Bund
- an excellent technical infrastructure and modern workplace equipment
- 6 weeks holiday per year; company holidays between Christmas and New Year's Day
- very good compatibility private and professional life; offers of mobile and flexible work
- PhD Buddy Program
- family-friendly company policy with childcare facilities, e.g. nursery close to the company
- free assistance program for employees (EAP)
- corporate benefits
- a varied offer in the canteen on campus
Severely disabled persons and those equaling severely disabled persons who are equally suitable for the position will be considered preferentially within the framework of legal requirements.
Interested?
Then we are looking forward to receiving your comprehensive application documents (cover letter, CV, transcripts, certificates etc.) indicating the reference number 2025/MO 2 until June 22nd, 2025.