Applied Mathematics - Quantum Machine Learning Internship

The HARP Research team is looking for 1 intern from a math/CS background to collaborate and help conduct groundbreaking research on quantum machine learning algorithms utilizing our novel graph neural network architecture.

We offer hybrid/remote opportunities to any accepted candidate.

Responsibilities:

• Conduct research in graph theory: Explore and develop new algorithms and models in graph theory and discrete mathematics.
• Apply graph theory to AI: Investigate applications of graph theory in AI and machine learning models.
• Collaborate on R&D projects: Work with other researchers and engineers on interdisciplinary projects involving graph theory.
• Publish research findings: Contribute to academic papers and presentations to share research findings.
• Prototype and test algorithms: Develop prototypes of new algorithms and test their effectiveness.
• Quantum Algorithm Development: Research and develop quantum machine learning algorithms leveraging graph neural networks.

Qualifications:

• Learning on the Fly: We are looking for individuals who can learn as they go, and pick up new skills as necessary
• Languages: Familiarity with C / C++ and Python. Familiarity with CUDA is highly desirable.
• Scientific Literacy: The ability to read and interpret scientific literature
• Communication and Organization: Proficient with GitHub issues and Discord
• Mathematics Background: A formal (or informal) educational background in discrete mathematics. Suggested fields include:
• Graph Theory/Network Theory
• Abstract Algebra/Group Theory
• Complex Analysis
• Linear Algebra
• Category Theory

• Topology

• Creative Thinking: Are you willing to take a crack at a new, previously unsolved problem?

• Tools and Libraries: Applicants with familiarity with the following are preferred:

• NetworkX

• Pytorch

• Docker

• Pytest

• SageMath

• Qiskit/Cirq