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Machining hard to cut materials like stainless steels induces high wear at the applied cutting tools. It is hard to predict the tool wear under changing engagement conditions. Either the tools are changed too early, wasting expensive resources, or the tools are changed too late, resulting in scraped parts. We will develop grey-box models to describe and predict tool wear, with a focus on extrapolation capabilities. This is part of a DFG funded project within the DFG priority program 2402.
We are looking for a highly motivated Ph.D. candidate to develop models integrating machine learning and domain-specific knowledge to predict tool wear. You will carry out computational model development, data processing, and code implementation in close cooperation with scientists from the TUHH Institute for Production Management and Technology and the Fraunhofer IST. Shared supervision is provided by Hereon (75%) and TUHH Institute of Mathematics (25%). The position is limited to three years.
Equal opportunity is an important part of our personnel policy. We would therefore stronglyencourage qualified women to apply for the position.
Your tasks
- application and development of active learning techniques to guide data acquisition strategy by project partners, data analysis
- training of black-box machine learning models, extension to grey-box models by incorporating traditional physics-based models
- identification and calibration of model parameters in close collaboration with the experimentalists of the project partners
- 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 computer science, mathematics, materials science, mechanical engineering, or similar
- basic programming skills in one or more languages (Python, C/C++, or others)
- profound knowledge of machine learning methods (e.g., neural networks, Gaussian processes, active learning)
- interest in materials science (e.g., wear-resistant coatings)
- 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 cutting tools
- international research experience
- willingness to learn German
We offer you
- an exciting and varied job in a research centre with more than 1,100 employees from around 50 nations
- a well connected research campus and best networking opportunities
- individual opportunities for further training
- social benefits according to the collective agreement of the public service and remuneration
- an excellent technical infrastructure and modern workplace equipment
- 6 weeks holiday per year and company holidays between Christmas and New Year's Day
- very good compatibility private and professional life through 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
Interested?
Then we are looking forward to receiving your comprehensive application documents (cover letter, CV, transcripts, certificates etc.) indicating the reference number code no.2023/MO 1 until 12th July,2023.
