Phd Position - Geesthacht, Deutschland - Helmholtz-Zentrum Hereon
Beschreibung
Beginn der Ausschreibung Ende der Ausschreibung Institut bzw.Bereich- Institut für Oberflächenforschung- Standort- Geesthacht- Bewerbermanagement- Frau Erika KrügerMachining 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.Your tasks:
- 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
- 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
- PhD Buddy Program
- free assistance program for employees (EAP)
- corporate Benefits
- a varied offer in the canteen on campus
Interested?
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