Master/PhD internship position – Machine learning algorithms & systems

Project description

The Cloud Storage and Analytics team at IBM Research – Zurich is conducting state-of-the-art research related to algorithms and systems for fast, large-scale machine learning. We are always looking to expand our team with highly talented MSc/PhD students interested in working in the area of novel algorithms and systems for machine learning. This position involves designing and/or prototyping new machine learning algorithms and evaluating their performance empirically and/or theoretically. Topics of interest include (but are not limited to) ensemble learning, distributed learning, boosting and automated machine learning.

Successful research will be published at top AI conferences and potentially integrated into IBM’s AI product offerings. The team has a proven track record in this area, having developed a new software framework for high-performance, distributed machine learning (Snap ML), and published research papers at top AI conferences.


Candidates are expected to have the following background and interests

  • BSc/MSc in Computer Science, Electrical Engineering, Applied Mathematics, Statistics or a related field (both MSc and PhD students are encouraged to apply)
  • Experience with C++ and/or Python
  • Practical experience with machine-learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
  • Familiarity with version control systems (git)
  • Publications at top AI conferences (NeurIPS, ICML, AISTATS, AAAI) or interest in publishing at such conferences (for MSc students)
  • Self-motivation and passion for problem solving
  • Excellent teamwork as well as written and oral communication skills

Positions are available immediately for a (flexible) duration of 3 to 6 months. The successful candidates will enjoy an internationally competitive salary and work in a collaborative and creative group in an exclusive research environment.


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

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