Machine learning

The successful candidate has competencies in machine learning within a broad spectrum of backgrounds and interests including, but not limited to:
- Active learning, clustering, online learning, ranking, reinforcement learning, supervised, semi- and self-supervised learning, and time series analysis;
- Deep Learning architectures, generative models, and deep reinforcement learning;
- Statistical Learning Theory; 
- Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods);
- Trustworthy Machine Learning (accountability, causality, fairness, privacy, robustness);
- Applications of machine learning to fields such as data science, economics, humanities, medical sciences, physical sciences, and social sciences.

 

3 months