Worldwide Borealis AI 2024 ML Research Internship Programme.

Borealis AI anticipates its internship programs to be in a hybrid work environment for the Winter 2024 term. Interns support research on a wide variety of theoretical and applied machine-learning projects.

Working at Borealis AI will grant you unique access to massive structured and unstructured datasets with the tools and resources necessary to build game-changing statistical models. Being part of the team means you’ll have the opportunity to publish original research in peer-reviewed academic conferences, such as NeurIPS, ICLR, ICML, and CVPR. And you’ll be working with some of the brightest minds in AI.

Internship opportunities are available in the following areas:

Deep Learning;
Reinforcement Learning;
Graphs and Optimization;
Unsupervised and Semi-supervised Learning;
Bayesian Optimization;
Privacy and Fairness;
Interpretability and Explainability;
AutoML;
Time Series Forecasting;
Natural Language Processing;
Computer Vision.

Benefits

Borealis AI will grant you unique access to massive structured and unstructured datasets with the tools and resources necessary to build game-changing statistical models.
Being part of our team means you’ll have the opportunity to publish original research in peer-reviewed academic conferences, such as NeurIPS, ICLR, ICML, CVPR. And you’ll be working with some of the brightest minds in AI.

Requirements

You’re an ideal candidate if you have:

Ability to implement state-of-the-art machine learning techniques;
High motivation to solve challenging research problems;
Passion for data, algorithms, and statistics;
Pursuing a graduate degree in Computer Science, Engineering or another mathematically related field (e.g., Physics, Math, Statistics, etc.);
Previous publications at a top-tier AI conference;
Experience with writing modular, robust, scalable software in Python;
Familiarity with the Unix command line and bash scripting;
Proficiency with deep learning packages, such as Tensorflow, Keras, and PyTorch;
A deep understanding of machine learning algorithms and/or statistical modeling.