Internship: Fault Diagnosis and Prognosis with DNN
DescriptionMERL is looking for a highly motivated intern to develop DNN-based methods for fault diagnosis and prognosis (i.e., prediction) problems, particularly for time series data. Successful candidate will collaborate with MERL researchers to design new algorithms, implement them, perform experiments, and prepare results for publication. The candidate should have a strong background in machine learning/statistics and deep learning. Knowledge and experience of any of the followings are desirable: generative adversarial networks, time series modeling and analysis, stochastic processes, and signal processing. The candidate should be comfortable implementing algorithms in Python. Prior experience with DNN packages such as Keras, TensorFlow, or Theano is a plus. The candidate is expected to be a PhD student in Computer Science, Statistics, Electrical Engineering, Applied Mathematics, or a related field. The duration of the internship is 12 weeks. The starting date is flexible and can be as early as April 2018.
Research Area: Data Analytics
Contact: Amir-massoud Farahmand