Borealis AI Winter 2024 ML Research Internship – APPLY NOW

The Borealis AI, an AI and Machine Learning Research Institute backed by the Royal Bank of Canada, offers an exciting ML Research Internship for the Winter 2024 term. This program presents a unique opportunity for students worldwide to engage in cutting-edge research on theoretical and applied machine-learning projects.

Borealis AI Winter 2024 ML Research Internship

Winter 2024 ML Research Internship

Interns at Borealis AI gain unique access to massive structured and unstructured datasets. This access is paired with the necessary tools and resources, enabling them to build game-changing statistical models that push the boundaries of machine learning research.

Being a part of the Borealis AI team provides interns with a platform to showcase their work on an international stage and contribute to the advancement of the field.

Additionally, Interns have the privilege of working alongside some of the brightest minds in the AI industry. The collaborative environment fosters creativity, innovation, and knowledge-sharing, leading to transformative research outcomes.

Available Internship Opportunities

Borealis AI offers a diverse range of internship opportunities, catering to various interests and specializations. Some of the areas open for internship applications include:

  • 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

About Borealis AI

Borealis AI, founded in 2016, is a leading AI and Machine Learning Research Institute comprising top AI researchers and engineers.

Backed by the Royal Bank of Canada, the institute aims to advance machine learning science while developing cutting-edge products for the financial services industry. With over 40 scientific publications in top-tier academic venues, Borealis AI continues to make significant contributions to AI research.

Eligibility Criteria for Borealis AI Winter 2024 ML Research Internship

To be considered an ideal candidate for the Borealis AI ML Research Internship, you should possess the following qualifications:

  • Be able to implement state-of-the-art machine learning techniques.
  • You must be motivated to solve challenging research problems.
  • Passion for data, algorithms, and statistics.
  • Pursuing a graduate degree in Computer Science, Engineering, or another mathematically related field.
  • Have Previous publications at a top-tier AI conference.
  • Posses Experience with writing modular, robust, scalable software in Python.
  • Should be Familiar with the Unix command line and bash scripting.
  • You must be Proficient with deep learning packages, including TensorFlow, Keras, and PyTorch.
  • Have an indebt understanding of machine learning algorithms or statistical modeling.

ML Research Internship Timeline

The Borealis AI ML Research Internship program spans four months, from January to April 2024.

The Application Deadline is September 4, 2023.

How to apply for the ML Research Internship 2024?

To apply for the ML Research Internship 2024, kindly visit the Borealis AI website at: www.borealisai.com/internship-applicationa

Provide all required information and Submit your application

FAQs

How long is the Borealis AI ML Research Internship?

The internship duration is four months, from January to April 2024.

Can I apply for multiple internship areas?

Yes, applicants can apply for multiple areas of interest, but they must tailor their application to each specific area.

Are remote internships available?

Yes, Borealis AI offers a hybrid work environment, allowing interns to work remotely or in person.

What should I include in my application?

Your application should highlight your qualifications, experience, and passion for machine learning research. Make sure to provide relevant publications and demonstrate your expertise in Python and deep learning frameworks.

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