Senior Lecturer in Machine Health Intelligence 80 - 100 %

School: School of Engineering
Starting date: 1st of September 2026 or upon agreement (Applications will be accepted until 30th of April 2026)

• Research and development:You will take strategic and scientific responsibility for the further development of the Machine Health Intelligence domain within the Institute for Data Science. As an independent investigator, you will establish and lead your own research line and actively acquire third-party funded research projects in collaboration with industrial and public partners. You will initiate, manage, and execute applied research and development projects in areas such as Intelligent Condition Monitoring, Predictive Maintenance, Machine Prognostics and Health Management (PHM), data-driven diagnostics, reliability engineering, and optimization-based decision support. You will publish your results in journals and present them in conferences (Innosuisse, EU, SNF, etc.).

• Teaching: You will design and deliver core and advanced courses at Bachelor's and Master's level. You will supervise project work, Bachelor's and Master's theses, and contribute to the continuous development of the study programs at the intersection of machine health and AI. You will contribute to the strategic development of continuing education formats, including CAS programs in Predictive Maintenance and related fields, and actively maintain a strong professional network across academia and industry.

• Collaboration: You will actively design the further development of the Machine Health Intelligence research team.

• You hold a doctoral degree in engineering, physics, applied mathematics, data science, or a closely related field, with a strong research background in one or more of the following:
  / Anomaly detection in physical or technical systems
  / Machine condition monitoring, diagnostics and prognostics.
  / Reliability and degradation modelling
  / AI-driven operational decision-making for complex technical systems
  / Physics-informed machine learning
  / Industrial AI and digital twins

• You have at least 3 years of experience in R&D in related fields, preferably in industry or the public infrastructure sector.

• You have demonstrated success in acquiring and managing externally funded research projects. A strong publication record in relevant journals and conferences is expected.

• You combine scientific excellence with practical orientation and are interested in translating advanced methods into industrial impact.

• You are passionate to transfer your knowledge and expertise to a next generation of engineers and data scientists, ideally with teaching experience in the higher education.

• You are fluent in English and German.

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.

As one of the leading educational and research institutions in Switzerland, the School of Engineering (SoE) focuses on topics relevant to the future. Fourteen institutes and centers ensure high-quality education, research, and development with a focus on the areas of energy, mobility, information, and health.

At the Institute of Data Science (IDS), we transform data into tangible added value. With a strong scientific foundation and a clear focus on practical solutions, we develop innovative approaches in Data Science and Business Engineering.

Our research team at the IDS focuses on intelligent data-driven solutions for machine health monitoring and optimization in industrial and infrastructure systems.
This position offers the opportunity to shape the future of these systems by leading an independent research program at the intersection of reliability engineering, artificial intelligence, optimization, and industrial digitalization.
We offer a strategically positioned role within a dynamic research and education environment that combines:

• Strong industry partnerships
• Applied research with real-world impact
• Academic freedom to shape your research line
• Interdisciplinary collaboration within data science and engineering
• Design and shaping of education programs at the intersection of data science and engineering
• A high degree of flexibility in working times

ZHAW is committed to gender-mixed and diverse teams in order to promote equality, diversity and innovation.