Research Assistant in Machine Learning for Complex Industrial Machines 100%

School: School of Engineering
Starting date: 1.9.2023 (The position is limited to one year but may be extended, up to a maximal period of three years)

Your role

• As part of our research team, you will take a central role in applied research projects in the field of condition-based and predictive maintenance.
• You will develop innovative approaches using state of the art statistical, machine learning and deep learning methods and publish your outcomes in scientific papers.
• You will develop and implement data-driven algorithms for detection, diagnostics and prognostics of incipient failures in industrial assets and infrastructures.
• You will combine engineering knowledge with data-science techniques (physics-informed machine learning).
• You will closely collaborate with our industry partners in order to implement your algorithms in the operational environment.
• You will have a chance to work on projects in diverse application fields with various industry partners.
• The focus of your work will be on algorithm research and development, with relatively little data management/engineering.
• You will have the opportunity to co-supervise student research projects.
• You will be supported by experienced colleagues from the team in order to further develop you technical and scientific skills.
• Your daily work will typically allow for a high level of flexibility and freedom.

In our team you will enjoy an exciting and rewarding working environment, combining close industrial collaboration with multidisciplinary innovative research.

The position is limited to one year but may be extended, up to a maximal period of three years.

Your profile

• You have a master degree in engineering, computer science, physics, statistics, applied mathematics or a related discipline.
• Alternatively, you have a bachelor degree in one of the above with practical experience in applied data science projects.
• You have programming experience in Python. Experience with TensorFlow and Keras/PyTorch is an advantage.
• You have experience with applying data analytical and machine learning methods.
• Experience with deep leaning algorithms is advantageous.
• You have high oral and written communication skills in English and preferably also in German.
• You are curious to learn and develop new methods and algorithms and apply them in real industrial systems.
• You are motivated to develop your personal research skills, coached by experienced researchers.

We encourage applications from proactive and self-motivated candidates with analytical thinking and strong problem-solving passion and abilities, good communication skills and original thinking.

This is what we stand for

Zurich University of Applied Sciences ZHAW is one of Switzerland's largest multidisciplinary universities of applied sciences, with over 14'000 students and 3'400 faculty and staff.

The Institute of Data Analysis and Process Design (IDP) is a center of competence for the development and appli-cation of quantitative methods in the areas of data analysis and operations research. The Smart Maintenance Team of the IDP develops innovative solutions for intelligent condition monitoring, fault detection, diagnostics and prog-nostics of technical equipment.

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