Many application domains increasingly require AD, when anomalies carry critical and actionable information. We shall address the problem of detecting and predicting general anomalies in high-dimension KPI performance metrics, i.e., high dimension and dynamic range multivariate non-stationary time series collected from large Cloud / IT environments. Using Keras / TF etc., we will build an ML-based AD framework for transfer, attention and meta-learning that must remain robust also with reduced/missing and noisy training data. Besides feature engineering -- e.g., selection, reduction, compression techniques -- explainability will also be necessary for the model prototype. The research is to be performed at IBM Research – Zurich Lab, Switzerland.

Arm's Machine Learning group are looking for an ML authority to shape our platform through technical mentorship. In this role you will collaborate with research, technology, engineering and marketing teams on solving big-picture problems whilst also providing detailed technical mentorship of teams developing specific use cases with other groups inside Arm. Knowledge of the theory and concepts involved in ML is important, as is a strong set of communication skills. The ideal person is someone with a keen problem solving mind, who is passionate about both state of the art machine learning and communicating that to others. In this dynamic field, being able to provide a clear view of current research trends along with the benefits and limitations of each will be the biggest challenge.