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Research Position at the Sharif Information Systems and Data Science Center
Project Description: Anomaly detection in time series on various datasets, including those related to autonomous vehicle batteries, predictive maintenance, and determining remaining useful life (RUL) upon anomaly detection in products, particularly electric vehicle batteries. The paper deadline for this project is by the end of February. The project also involves the use of federated learning algorithms to support multiple local devices in anomaly detection, RUL estimation, and predictive maintenance on each local device.
Technical Requirements: Two electrical or computer engineering students with strong skills in deep learning, robustness concepts, time series anomaly detection, federated learning algorithms, and a creative mindset, strong and clean implementation skills.
Benefits: Access to a new, well-equipped lab and Research under the supervision of three professors in Electrical and Computer Engineering.
Dr. Babak Khalaj
Dr. Siavash Ahmadi
Dr. Mohammad Hossein Rohban
Please send your CV, with the subject line "Research Position in Time Series Anomaly Detection,"
to the email address: data-icst@sharif.edu.
BY RIML Lab
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