Zurich University of Applied Sciences

School of

PhD Candidate in Data Science: Machine Learning for Analysis of Nuclear Magnetic Resonance (NMR) Spectra

Zurich University of Applied Sciences ZHAW is one of Switzerland’s largest multidisciplinary universities of applied sciences, with over 13,000 students and 3,000 faculty and staff.

As one of the leading Engineering Faculties in Switzerland, the ZHAW School of Engineering emphasises topics which will be relevant in the future. Our 13 institutes and centres guarantee superior-quality education, research and development with a focus on the areas of energy, mobility, information and health.

Since 2013 the Institute of Applied Mathematics and Physics (IAMP) has teamed up with five other institutes from three departments to establish one of the first interdisciplinary Data Science Centres in Europe – the ZHAW Datalab (www.zhaw.ch/datalab ). It runs a focus area on deep learning methodology, spanning several research groups, with several multi-year research projects on the applicability of reinforcement learning to areas from home automation to applications in medical technology.

Within the Research Group Applied Complex Systems Sciences of the Institute of Applied Mathematics and Physics (www.zhaw.ch/iamp ), we are looking for a highly motivated and excellent candidate as:

Your responsibilities:

  • Develop machine learning tools for the analysis of NMR spectra
  • Contribute to an Innosuisse research project of ZHAW and Bruker BioSpin AG
  • Become an expert in the field of machine learning for NMR technology, with a track record of published own research results and successful technology transfer
  • Apply for the PhD Program in Data Science at University of Zurich (UZH). The candidate will receive a PhD degree from University of Zurich

The successful candidate has finished a Master’s degree in physics, physical chemistry, or computer science at a research university or a university of applied sciences within the last two years. He or she must have reliable experience in neural networks (e.g., done coursework and own experimentation, ideally proven by successful projects or publications). We expect a good command as well as a passion for programming, ideally in python. Good communication skills in English and German are also crucial for the work in multidisciplinary teams. The ideal candidate has sound knowledge of Nuclear Magnetic Resonance and is interested in understanding the underlying quantum mechanics. The PhD candidate will be part of an interdisciplinary team of researchers at ZHAW and Bruker BioSpin AG working together on an Innosuisse project. Hence, particular emphasis is given to developing prototypes, experimenting hands-on with data, and scientific evaluation and publication of results.

We offer a competitive, technically stimulating, and exciting environment at ZHAW with the possibility to obtain a PhD degree from University of Zurich in the PhD program in Data Science. The PhD candidate will be jointly supervised by Prof. Dr. Dirk Wilhelm at ZHAW and Prof. Dr. Roland Sigel at University of Zurich. The position will involve publishing, for example by writing papers, presenting at scientific conferences, etc. As well as providing an excellent work environment, we provide intellectual liberty and competitive compensation based on Swiss standards at one of the leading houses for data science research in Switzerland.

Do you have any questions about this position?
For additional information, please contact Prof. Dr. Dirk Wilhelm, Dean ZHAW School of Engineering, phone +41 58 934 4729, email: dirk.wilhelm@zhaw.ch, or Prof. Dr. Rudolf M. Füchslin, Head of Applied Complex Systems Science Team, phone +41 58 934 75 92, email: rudolf.fuechslin@zhaw.ch .

Are you interested?
If you would like to apply, please use the online platform to send your full application. Applications will be evaluated on a rolling basis starting from Nov. 20. Please be advised that we only consider applications via our online-platform and not by mail or by email.

Find more information: www.zhaw.ch/engineering

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