Summer internship in machine learning

Are you interested in research and machine learning? We are looking for 1-2 summer interns to join the Multi-source Probabilistic inference group ( to work at the intersection of machine learning methods development and data science applications. We work on a range of interesting applications and your interests are taken into account when deciding what you would be working on. Examples of current domains include ultrasound physics, mobile hyperspectral imaging (, game design and economy.

We appreciate good programming skills and strong mathematical background, including linear algebra, probability and differential calculus. We expect that you have completed some courses in machine learning and/or artificial intelligence, at least at the level of Introduction to Machine Learning. Candidates with background in computational physics, applied mathematics, statistics and other computational fields are also encouraged to apply.

Interest and existing knowledge of Bayesian methods, probabilistic programming and deep learning is considered a strong advantage. We work mainly with Python, using common machine learning libraries such as TensorFlow and PyTorch. We’ll show you the ropes if you aren’t already familiar with these, but prior knowledge is useful.


Applications are sent through the Department of Computer Science summer intern call by February 4th, 2018.


Last updated on 1 Feb 2018 by Arto Klami - Page created on 17 Jan 2018 by Arto Klami