www.acad.jobs : academic jobs worldwide – and the best jobs in industry
                
     
Position: PhD Position in Deep Learning for Prognostics of Complex Engineered Systems
Institution: Zürcher Hochschule für Angewandte Wissenschaften
Location: Winterthur, Zürich, Switzerland
Duties: Prognostics models capable of efficiently forecasting the time to failure of complex engineered systems are key enablers of predictive maintenance strategies. The combination of physical and deep learning models is one of the most promising techniques to obtain accurate, robust, and interpretable models for prognostics; The focus of the PhD will be on physics-informed deep learning algorithms for prognostics of complex engineered systems. This includes developing novel neural network algorithms that can exploit physics and engineering knowledge of the system together with available data to improve prognostics
Requirements: An excellent MSc degree in engineering, computer science, statistics, applied mathematics, physics, or related field; Experience with applying data analytics and machine learning methods; Experience with deep learning algorithms, statistics, and learning theory; Strong programming experience in Python or similar; Nice to have: Experience with physics-informed learning, graph neural networks (GNN), generative adversarial networks (GAN) or physics-constrained generative neural networks; Fluency in English (both written and spoken) is mandatory
   
Text: PhD Position in Deep Learning for Prognostics of Complex Engineered Systems 100 % You want to shape the field of deep learning for prognostics of complex systems? Then this vacancy at the Smart Maintenance Team of ZHAW and the Faculty of Aerospace Engineering at TU Delft will surely interest you. School: School of Engineering Starting date: This position is available immediately or upon agreement. Your role Prognostics models capable of efficiently forecasting the time to failure of complex engineered systems are key enablers of predictive maintenance strategies. The combination of physical and deep learning models is one of the most promising techniques to obtain accurate, robust, and interpretable models for prognostics. The focus of the PhD will be on physics-informed deep learning algorithms for prognostics of complex engineered systems. This includes developing novel neural network algorithms that can exploit physics and engineering knowledge of the system together with available data to improve prognostics. Your profile We encourage applications from proactive and self-motivated candidates with analytical thinking, strong problem-solving passion and abilities, good communication skills, and original thinking. A successful candidate also has: An excellent MSc degree in engineering, computer science, statistics, applied mathematics, physics, or related field Experience with applying data analytics and machine learning methods Experience with deep learning algorithms, statistics, and learning theory Strong programming experience in Python or similar Nice to have: Experience with physics-informed learning, graph neural networks (GNN), generative adversarial networks (GAN) or physics-constrained generative neural networks Fluency in English (both written and spoken) is mandatory The PhD project will be part of a research project funded for max. 4 years. As part of our team, you will enjoy an exciting and rewarding international working environment and innovative multidisciplinary research. The compensation will be according to the guidelines of the Swiss National Science Foundation (www.snf.ch). Are you interested? If you would like to apply, please use the online platform to send us your complete application: a cover letter indicating your motivation and expectations from this research, CV, one publication (e.g. thesis or preferably a conference or journal publication), transcripts of all obtained degrees (in English) and the contact details for two referees. Jetzt online bewerben ­ 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. As one of the leading Engineering Faculties in Switzerland, the emphasizes topics that will be relevant in the future. Our 14 institutes and centers guarantee superior-quality education, research, and development with a focus on the areas of energy, mobility, information, and health. The is a center of competence for the development and application of quantitative methods in the areas of data analysis and operations research. The Team at IDP develops solutions for intelligent condition monitoring, fault detection, diagnostics, and prognostics of technical equipment. Our focus is on developing state-of-the-art research ideas for real-world operational systems. Our project partners include industrial companies, software companies, and the public sector. In collaboration with the Air Transport & Operations (ATO) group at the Delft University of Technology (TU Delft) and Palo Alto Research Center (PARC), we are now looking for an outstanding PhD student to conduct one of these projects. The supervision of this PhD project is shared between the Institute of Data Analysis and Process Design ( ) at the ZHAW and the Air Transport & Operations (ATO) group (Dr. Marcia L. Baptista) at the Delft University of Technology. Upon successful completion of the PhD program, the doctoral degree will be awarded by the Delft University of Technology. Das dürfen Sie erwarten Wir bieten hochschulgerechte Arbeits- und Anstellungsbedingungen und fördern aktiv die Personalentwicklung unserer Mitarbeitenden und Führungspersonen. Eine detaillierte Beschreibung der Vorteile finden Sie auf der Seite . Hier die wichtigsten Eckpunkte: Contact Dr. Manuel Arias Chao Research Associate Nadine Mueller Recruiting Manager 2022-01-26 Permanent employment ZHAW Zürcher Hochschule für Angewandte Wissenschaften Winterthur Region of Zurich 8401
Please click here, if the job didn't load correctly.
Your browser does not support iframes. Please click <a href="https://www.acad.jobs/job.php?t_id=J000359836&redirect" target="_parent" style="color:#7A7A7A">here</a>, if the job didn't load correctly.