Ph3Overview /h3pAre you passionate about pushing signal processing further? Are you fascinated by combining it with deep learning methods to build real-world solutions deployed in operational railway systems? If yes, this role is for you. /ppbSchool: /b /ppSchool of Engineering /ppbStarting date: /b /pp1 June 2026 or upon agreement /ph3Your role /h3ulliAs part of our research team, you will play a central role in applied research projects on AI for intelligent machine health. /liliYou will develop innovative methods using state-of-the-art signal processing, machine learning, and deep learning techniques, and publish your results in scientific journals and conferences. /liliYou will combine engineering knowledge with advanced signal processing and data science approaches. /liliYou will design and implement algorithms for early detection and diagnostics of failures in railway systems. /liliYou will collaborate closely with industry partners to transfer and deploy developed algorithms in real operational environments. /liliYour work will primarily focus on algorithm research and development, with relatively limited data engineering or management tasks. /liliYou will have the opportunity to co-supervise student research projects and contribute to the development of junior researchers. /liliYou will be supported by experienced colleagues, enabling you to further develop your technical and scientific expertise. /li /ulpThe position offers a high degree of flexibility and autonomy in your daily work. You will join a dynamic and rewarding research environment that combines multidisciplinary innovation with strong industrial collaboration. The position is initially limited to two years, with the possibility of extension up to a maximum of three years. /ph3Your profile /h3ulliYou hold a Master’s or PhD degree in Engineering, Physics, Computer Science, Statistics, Applied Mathematics, or a related field. /liliYou possess strong knowledge and hands-on experience in signal processing, including methods such as Fourier, Wavelet, and Hilbert transforms. Experience in audio signal processing is considered an advantage. /liliYou have programming experience in Python; familiarity with TensorFlow, Keras, or PyTorch is a plus. /liliYou have experience applying data analytics and machine learning techniques; experience with deep learning algorithms is advantageous. /liliExperience working with real-world, unsupervised data environments is beneficial. /liliYou demonstrate strong written and verbal communication skills in English. /liliYou are curious and motivated to develop new methods and algorithms and apply them in real industrial systems. /liliYou are eager to develop your research skills under the guidance of experienced researchers. /liliWe welcome applications from proactive and self-motivated candidates who demonstrate strong analytical thinking, excellent problem-solving abilities, effective communication skills, and originality in their approach. /li /ulh3This is what we stand for /h3pZurich 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 develop cutting edge solutions for condition-based-maintenance of railway systems at the interface of signal processing and machine learning together with a multidisciplinary team of experienced researchers and industry experts. ZHAW is committed to gender-mixed and diverse teams in order to promote equality, diversity and innovation. /ph3What you can expect /h3pWe offer working conditions and terms of employment commensurate with higher education institutions and actively promote personal development for staff in leadership and non-leadership positions. A detailed description of advantages and benefits can be found at Working at the ZHAW. The main points are listed below: /ph3Contact /h3pbDr. Lilach Goren Huber /b Senior Lecturer, Machine Health Intelligence /ppbIrina Keiser /b Recruiting Manager /p /p #J-18808-Ljbffr