Research Assistant Professor

Job description

Description The successful candidate will design and implement a new anomaly detection method in collaboration with Computer Science professor Nathalie Japkowicz and Statistics professor Michael Baron. This work will combine neural network and machine learning techniques with statistical learning methods. The resulting system will automatically recognize anomalies from stream data in real time, classify, and adapt to them. Requirements: The ideal candidate will hold a Ph.D. in Computer Science in the area of Machine Learning and Neural Networks. S/he must have an interest and some experience in a variety of machine learning techniques such as one-class learning or outlier detection, approaches to deal with the class imbalance problem, big data analysis tools such as Spark, time-series analysis and data stream learning techniques. S/he must have experience or interest in integrating Statistical methods in the system and be interested in collaborating with a team of statisticians. S/he will have a record of publications in well-recognized conference and/or journal venues. The principal duties to be carried out include: - The design of the Lifelong Streaming Anomaly Detection system’s...