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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: [email protected].



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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: [email protected].

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Telegram and Signal Havens for Right-Wing Extremists

Since the violent storming of Capitol Hill and subsequent ban of former U.S. President Donald Trump from Facebook and Twitter, the removal of Parler from Amazon’s servers, and the de-platforming of incendiary right-wing content, messaging services Telegram and Signal have seen a deluge of new users. In January alone, Telegram reported 90 million new accounts. Its founder, Pavel Durov, described this as “the largest digital migration in human history.” Signal reportedly doubled its user base to 40 million people and became the most downloaded app in 70 countries. The two services rely on encryption to protect the privacy of user communication, which has made them popular with protesters seeking to conceal their identities against repressive governments in places like Belarus, Hong Kong, and Iran. But the same encryption technology has also made them a favored communication tool for criminals and terrorist groups, including al Qaeda and the Islamic State.

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