MERL is looking for a self-motivated intern to develop anomaly detection algorithms with applications in time series data. The ideal candidate is a senior PhD student with experience in methods applied to time series data including deep learning methods (AE, VAE, GAN, etc.) or experience in classical machine learning. Preferred candidates will have a background working with data outside computer vision. The candidate should have strong programming skills using Python and at least one publication. Successful internships at MERL result from intern driven algorithm development that leads to a scientific publication. Typical internship length is 3 months with early start dates preferred (ex. late April, early May). The internship is also available for the Fall of 2021. Please specify your preferred start date when applying.
- Research Areas: Data Analytics
- Host: Emil Laftchiev
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