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 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.
- 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.
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.
To apply for this job please visit www.zurich.ibm.com.
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