PhD Student in AI research

PhD Student in AI Research

We are seeking a highly talented PhD Student with experience and interest in state‑of‑the‑art AI technologies, data science, and research software engineering. This position offers the opportunity to work in a world‑renowned research institute alongside faculty, software engineers, PhD students, and Post Docs on the development of cutting‑edge AI‑ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence.

The project will explore conceptual frameworks and system architecture to support data trust in the context of AI‑enabled science workflows. Experience in developing and deploying software is desired.

We are extending and maintaining the National Data Platform to support a new generation of discovery capabilities targeting AI‑enabled (agentic) science workflows. Building on ideas pioneered by the Democratizing Data project, this project will explore approaches to discover data and models that are trusted and fit for use by leveraging contextual information about their use derived from a range of sources including published literature.

The approach includes extracting, linking, and presenting evidence from records to help users understand where models are used, how they are applied, what tasks they support, and how they evolve across scientific domains.

The successful candidate will contribute directly to the research and implementation of workflows for AI model identification, publication mining, metadata engineering, search and retrieval, and interactive user‑facing systems. The role includes support for integration, development, deployment, maintenance, UI/UX, containerization, automation, and system‑level optimization.

Contact Jess Tate (jessh.edu) for further information.

This position centers on the design and development of systems that enable discovery and analysis of AI models used in scientific research publications. Rather than cataloging datasets cited by papers, this effort focuses on identifying and organizing information about:

• AI models mentioned or used in publications
• Model families, versions, checkpoints, and architectures
• Tasks and scientific domains in which models are applied
• Evidence of reuse, benchmarking, fine‑tuning, and comparison
• Links among papers, models, repositories, benchmarks, and supporting resources
• Search, ranking, and visualization interfaces for model‑centric scholarly exploration

This work sits at the intersection of AI/NLP, scholarly knowledge extraction, software engineering, data infrastructure, and human‑centered discovery systems.

Through the Cyberinfrastructure Professionals (CIP) Cooperative at the SCI Institute, the successful candidate will access a strong community of research computing and data experts working together to support and sustain SCI's world‑class research efforts. This role offers exceptional opportunities to:

• Contribute to high‑impact interdisciplinary research
• Publish and present work in relevant venues
• Collaborate with experts in AI, data systems, and scientific computing
• Gain hands‑on experience with production‑grade research software and infrastructure
• Mentor students and junior developers
• Shape the future of AI‑ready scholarly discovery platforms

• Flexible working hours
• Professional career development opportunities
• Collaboration with a multidisciplinary and research‑driven team
• Access to strong computing and research infrastructure
• A competitive benefits package through the University of Utah

• Work with faculty, staff, and students in designing and developing computational tools in support of research projects.
• Aid in the specification of software requirements in coordination with faculty or team leads.
• Develop user interfaces and APIs with web front‑end technologies such as HTML5, SCSS, Vue, React, Typescript, D3.js, Node.js, Chart.js.
• Build back‑end server components with technologies such as Flask, Django, CKAN, ArangoDB, PostgreSQL, MongoDB, Redis, and Elasticsearch.
• Create and maintain build, testing, and deployment systems with Git, Git Hub, Slack, Docker, AWS,…