Research Professional I, Computer Science (Temporary)

The University of Arizona • Tucson, AZ

2d • Onsite

About The Position

The University of Arizona's department of Computer Science invites you to apply to the role of Research Professional I. Under general supervision, the employee will implement existing Monte Carlo Tree Search based reinforcement learning algorithms and evaluate it in popular, publicly-available benchmarking suite, and compare it with other non-Monte Carlo Tree Search based baselines. The University of Arizona’s Department of Computer Science offers a collaborative environment focused on high-quality research and instruction, where undergraduate and graduate students benefit from the department’s research programs. Established in 1971, the department offers bachelor’s, master’s, and doctoral degrees and has a distinguished reputation for research excellence across areas including algorithms, artificial intelligence, high-performance computing, programming languages, security, systems, and visualization. The department is nationally recognized for its research impact and ranks among the top public Ph.D.-granting computer science programs in the nation. The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocations services, please click here.

Requirements

• Bachelor's degree or equivalent advanced learning attained through experience required.
• 1 year of relevant work experience is required.

Nice To Haves

• Skilled in maintaining code for libraries, APIs, deep learning, and reinforcement learning pipelines.
• Ability to generate evaluation plots on the performance of algorithms, and visualize the reasoning traces of Monte-Carlo Tree Search agents
• Ability to translate technical concepts into clear documentation and collaborate across multidisciplinary teams.

Responsibilities

• Implement the basic Monte Carlo Tree Search algorithm and evaluate it in publicly-available benchmark reinforcement learning environments such as OpenAI Gym.
• Implement basic imitation learning algorithms such as supervised finetuning (SFT) and evaluate them in benchmark reinforcement learning environments.
• Implement an reinforcement learning algorithm using the outcomes of tasks 1 and 2, and prepare evaluation reports for this algorithm in benchmark reinforcement learning environments.
• Maintaining source code of the training pipelines, such as documentation of the source code, preparing usage manuals, and identification of library dependencies.
• Collaborate with internal departments to share information and improve data handling processes.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level