The Borealis AI 2024 ML Research Internship Program
The Borealis AI 2024 ML Research Internship Program is a unique program that seeks to attract and develop top Machine Learning talent worldwide. Borealis AI expects 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.
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.
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ELIGIBILITY CONDITIONS FOR THE 2024 ML RESEARCH INTERNSHIP PROGRAM
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.
MERITS OF INTERNSHIP
- Borealis AI will give you unique access to massive structured and unstructured datasets with the tools and resources needed 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, and CVPR. And you’ll be working with some of the brightest minds in AI.
- The internship duration is 4 months from January – April 2024.
Application Deadline: September 4, 2023
OR KINDLY