Master’s student or intern

Anomaly Detection with Explainability & Causality on High-dimensional Time Series

Ref. 2022_006


Many application domains increasingly require AD, when anomalies carry critical and actionable information. These include:

  • Cyber-security and intrusion detection in Cloud and IT systems, also in government, defense and security agencies
  • Fraud detection in financial institutions
  • Manufacturing, IoT, industry and resource exploration
  • Healthcare etc.


We shall address the problem of detecting, predicting and explaining general anomalies in high-dimension KPI performance metrics, i.e., high cardinal and large dynamic range multivariate non-stationary time series collected from real 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 and Causality will also be necessary for the ML model prototype.


  • Data science/mining in general, multivariate timeseries in particular, also including logs and tickets (NLP mappings, embeddings);
  • Feature engineering and DL experience with RNN/CNN/TCN-based xAutoencoders in particular;
  • Hands-on experience with deep neural network models in Python, NumPy, Pandas, SciPy etc. applied to deep RNN/CNN/Autoencoders, ideally also including Explainability and Causality methods;
  • Motivation to learn real-life time series and experiment with DL in Keras/TensorFlow/PyTorch etc.

About the position

The research is to be performed remotely & on campus at IBM Research Europe – Zurich in Rüschlikon, Switzerland.
The expected duration is 3–9 months, starting as soon as possible.


IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

If you are interested in this position, please submit your application below.

Questions? For more information, please contact Mitch Gusat, .