Job Detail

Master’s student position or internship

Machine learning / Deep learning

Anomaly detection on high-dimensional time series

Ref. 2019_026

Background

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.

Project

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.

Requirements

• Data science/mining in general


• Feature engineering and DL experience with RNN/CNN/xAE in particular


• Hands-on experience with deep neural network models in Python, NumPy, Pandas, SciPy etc., applied to deep RNN/CNN/Autoencoders


• Motivation to learn real-life time series and experiment with DL in Keras/TensorFlow/ PyTorch

About the position

The research is to be performed at IBM Research – Zurich Lab, Switzerland.

The expected duration is 3–6 months, starting as soon as possible from June 2019.

Diversity

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

Interested? Please contact Dr. Mitch Gusat, mig@zurich.ibm.com.

Connect with us

Downloads

• Overview presentation of IBM Research – Zurich Lab (7 MB)


• IBM Research – Zurich Lab Fact Sheet (1.5 MB)


• IBM’s Diversity brochure (168 KB)