The Quantum Algorithms and Quantum Machine Learning Intern 2027 joins a collaborative research team to develop new methods for learning and simulation at the intersection of quantum computing, machine learning, and scientific computing. The intern will develop and evaluate quantum-classical methods, implement research prototypes, and benchmark accuracy and scalability against strong classical methods. Key tasks include applying these techniques to problems in physics, chemistry, and materials science while analyzing where quantum resources provide a meaningful advantage over classical counterparts. Candidates should possess a strong foundation in linear algebra, probability, and optimization, alongside experience with Python, Qiskit, and machine-learning frameworks. The role focuses on solving complex scientific challenges by exploring hybrid learning workflows and algorithms for simulating physical systems, requiring the ability to communicate technical findings through reports, talks, and open-source code contributions.
Develop quantum and hybrid algorithms for scientifically meaningful learning or simulation tasks.
Implement research prototypes to benchmark accuracy, resource requirements, and scalability against classical methods.
Apply quantum-classical methods to problems in physics, chemistry, materials science, or related domains.
Analyze when quantum resources provide a measurable advantage over classical counterparts.
Communicate technical findings through reports, talks, open-source code, and potential conference submissions.
Conduct independent research and critically analyze results regarding quantum machine learning and simulation.
What we're looking for
Master's Degree (preferred).
Enrollment in a graduate program in quantum computing, computer science, physics, applied mathematics, statistics, information theory, engineering, or a related field.
Strong foundation in quantum computing and algorithms with knowledge of linear algebra, probability, optimization, or numerical methods.
Research experience in machine learning, quantum machine learning, quantum simulation, or hybrid quantum-classical methods.
Experience implementing research code in Python or a comparable scientific programming language.
Ability to conduct independent research, analyze results critically, and communicate technical findings clearly.
Familiarity with Qiskit, scientific Python libraries, machine-learning frameworks, Git/GitHub, or experience with quantum processors (preferred).
Python
PyTorch
TensorFlow
Hugging Face
LLMs
RAG
AI Agents
Natural Language Processing
Distributed Systems
Linux
IBM Z
z/OS
Memory Management
Information Retrieval
Inference Optimization
Cloud Infrastructure