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PhD position - AI Machine learning for electric discharge machining in Zürich

Studierende finden an der ETH Zürich ein Umfeld, das eigenständiges Denken fördert, Forschende ein Klima, das zu Spitzenleistungen inspiriert.


100%, Zurich, temporary

The Institute of Machine Tools and Manufacturing (IWF) at ETH Zürich focuses on fundamental and applied research in many areas of production engineering. Headed by Prof. Dr. Konrad Wegener, the fields of activity range from production machines, manufacturing processes and machine simulation to virtual reality. The IWF works in close cooperation with inspire AG, the leading Swiss competence centre for technology transfer to the mechanical, electrical and metal industries and many other industry partners. To strengthen our process group, we are offering the following position for young scientists (m/f).

Project background

Electric discharge machining is one of the most important process technologies for the production of tools, moulds, and dies, and enables efficient mass production. Hardly any device of daily life can do without this technology today. Due to the extreme process conditions, temperatures above 10000 K, high spatial and temporal derivations, and multiple phase (dielectric, plasma and melt), the understanding of the processes, which is the basis for process improvement, is still limited. At the IWF, this interesting process has been researched for years and new results are constantly being obtained that are helpful for process optimisation. Neither metrological nor numerical simulations alone are sufficient to understand and describe electrical discharge machining. For a better understanding of the process, simulation and experimental work must be combined sensibly. State-of-the-art experimental machines, powerful measurement and simulation technology are available at IWF and are to be further developed within the framework of the doctorate.

Job description

In order to optimize the EDM process, a new generation expert system is to be developed. With the help of "artificial intelligence", machine learning and data analytics on top of physics based modelling, practice-relevant efficient machining strategies can be developed based on a physically based process model.

Your profile

You meet the requirements for a doctoral programme at ETH Zürich and have a Master's or diploma degree in mechanical engineering, materials engineering, physics or computer science. You are highly motivated and eager to learn. In addition, you like to combine theoretical considerations and models with experimental tasks and would like to be involved in teaching at IWF. Accurate work and very good communication skills in German and English round off your profile. We offer you a full-time position for the duration of your doctoral studies. Through close cooperation with our project partners you will quickly establish contacts with the Swiss industry. In our young team you will work at the cutting edge of today's knowledge in production and manufacturing technology.

ETH Zurich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

We look forward to receiving your online application with the following documents:

  • Curriculum Vitae
  • Motivational Letter
  • Transcript of records
  • Ev. reference letters

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about D-MAVT and IWF can be found on the following websites:

  • D-MAVT: 
  • IWF: 

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