In this role, you will:
Lead the design, implementation, and evaluation of AI/NLP systems for scientific feasibility assessment; develop and test integrated pipelines combining literature-based discovery, structured knowledge extraction, simulation, and code-based experimentation; run experiments and benchmarking at scale.
Architect and maintain high-quality research codebases in Python and related tools; implement automated experimentation frameworks, simulation integrations, and evaluation infrastructure; ensure reproducible artifacts, logging, and documentation for large-scale empirical studies.
Design and execute systematic empirical analyses of model behavior and experimental outcomes; analyze large-scale datasets, intermediate outputs, and logs to diagnose system performance, refine methods, and ensure research rigor.
Lead and co-author conference and journal papers for top-tier AI/NLP and machine learning venues; prepare manuscripts for peer-reviewed publication; create clear technical presentations and present findings at national and international conferences.
Develop research directions, experimental methodology, and project roadmaps; assist with preparation of technical reports, and research documentation as needed.
Drive collaboration, manage relationships, and provide technical guidance to student researchers where appropriate.
Contribute to a collegial, respectful, and collaborative research environment through professional and constructive communication.
Coordinate with collaborators within and outside the University as needed for joint experiments, data integration, and research dissemination.
Knowledge, Skills, and Abilities:
Exceptional programming ability, particularly in Python, and strong software engineering practices.
Demonstrated ability to design and maintain substantial research codebases.
Strong knowledge of AI/NLP research methods, including experimental design, evaluation, and benchmarking.
Strong demonstrated ability at hands-on empirical research, including extensive interaction with generating and analyzing large volumes of data, intermediate representations, and experimental outputs.
Familiarity with Linux-based research environments, Git, containerization (e.g. Docker), and reproducible workflows.
Ability to work independently in a research-driven, small-lab setting.
Strong written and verbal communication skills.