Research Associate - School of Computer Science - MLD

The Machine Learning Department (MLD) at Carnegie Mellon University is a leading hub for research and education in artificial intelligence and machine learning. It focuses on developing innovative algorithms and models to address complex problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs, fostering a collaborative environment that bridges theoretical research and practical applications. Faculty and students frequently collaborate with industry and other academic disciplines to push the boundaries of what is possible with machine learning.

MLD is seeking a Research Associate to conduct research in Casual Representation Learning for anomaly detection.

Core Responsibilities

• Implement and test approaches for representation learning using modern deep learning frameworks like PyTorch or TensorFlow.
• Design and run experiments to evaluate approaches on both synthetic and real-world datasets.
• Collaborate with team members to debug models, analyze results, and iterate on research directions through regular code reviews and research discussions.
• Document research findings through clear technical writing, including experiment logs, methodology descriptions, and contributions to research papers.
• Participate in regular research meetings to present findings, discuss relevant papers, and contribute to brainstorming sessions.

Adaptability, excellence, and passion are vital qualities within Carnegie Mellon University. We are in search of a team member who can effectively interact with a varied population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work.

Qualifications:

• Bachelor's Degree required. Master's Degree preferred.
• Previous research experience preferred.
• Strong analytical, communication, problem-solving, and reasoning skills.
• A combination of education and proven experience from which comparable knowledge is demonstrated may be considered.

Requirements

• Successful completion of a pre-employment background check