Research Associate - School of Computer Science - MLD

Carnegie Mellon University•Pittsburgh, PA

5d•Onsite

About The Position

Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the curious to deliver work that matters, your journey starts here! 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.

Requirements

Bachelor’s Degree required.
Successful completion of a pre-employment background check

Nice To Haves

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.

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.