If you are fluent in 2 or more of { category theory, Haskell (Idris/Agda/...), deep learning }, you'll probably have a lot of fun with us! We are hiring interns to explore the application of category theory, dependent type theory, and functional programming to deep learning.
We're trying to apply the insights of category theory, dependent type theory, and functional programming to deep learning. How do we best equip neural nets with strong inductive biases from these fields to help them reason in a structured way?
