Job Description

The ALCF has an opening for a postdoctoral position in AI-assisted scientific visualization and intelligent data exploration. The successful candidate will join the Visualization and Data Analytics team and contribute to IDEAS (Intelligent Data Exploration Assistant for Science), a multi-year research effort focused on developing AI-powered visualization systems that enable interactive, human-in-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human-computer interaction (HCI), and high-performance computing (HPC). The postdoc will work closely with visualization researchers, AI scientists, and domain application teams across Argonne and the broader DOE ecosystem. The goal of this postdoctoral position is to design, develop, and evaluate AI-driven scientific visualization assistants that support intuitive, context-aware interaction with large-scale simulation and experimental data. The postdoc will focus on the LLM/agent and HCI layers of IDEAS, including: Mixed-initiative AI assistants for visualization and analysis, Natural interaction paradigms for scientific workflows, Integration of agentic systems with advanced visualization pipelines, and Evaluation of trust, usability, and effectiveness of AI-assisted exploration. The successful candidate will: Research and develop AI-powered visualization assistants that explain, guide, and support scientific data exploration without replacing human judgment Design human-centered interaction models for AI-assisted visualization, including natural language, mixed-initiative workflows, and immersive or experimental interfaces Integrate LLM-based and agentic AI systems with scientific visualization frameworks, in situ pipelines, and data analysis workflows Prototype and evaluate visualization systems using realistic scientific data and simulations, including applications in cosmology, materials, fusion, environmental science, and light source data Collaborate closely with domain scientists to understand exploration needs and translate them into effective AI-assisted visualization tools Publish and present research results at leading venues in visualization, AI, and HPC (e.g., IEEE VIS, LDAV, eScience, ISC, SC) Contribute software, prototypes, and design patterns to shared research infrastructure at ALCF and across DOE projects This postdoctoral position directly supports ALCF’s mission to advance AI-enabled scientific discovery. The work will: Enable new modes of interactive exploration for exascale simulation and experimental data Inform the design of future visualization systems and AI services for leadership computing facilities Strengthen ALCF’s role in shaping human-centered AI systems for science, bridging visualization, AI, and HPC Contribute to DOE-wide efforts in AI-ready workflows, reproducibility, and scalable data understanding Position Requirements PhD completed within the last 0-5 years (or near completion) in Computer Science, Computational Science, Visualization, Human-computer Interaction, or a related field Strong background in scientific visualization, visual analytics, or interactive data exploration Experience programming in one or more languages such as Python, C/C++, or JavaScript Experience with AI or machine learning techniques, including large language models or agentic systems Experience developing or integrating interactive visualization systems, including web-based, immersive (XR), or experimental display environments Ability to create, maintain, and support high-quality research software Experience working collaboratively in multidisciplinary research teams Effective written and oral communication skills Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Skills and Qualifications Experience with in situ visualization, data streaming, or real-time analysis workflows Experience with LLMs, conversational agents, or AI-assisted interfaces for scientific or data-driven applications Familiarity with HPC environments, distributed systems, or large-scale data pipelines Experience with game engines, XR systems, or advanced graphics frameworks applied to scientific data Record of publications in visualization, AI, or computational science venues Interest in working closely with domain scientists on real-world scientific problems Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full timeThe expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package. As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law. Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment. #J-18808-Ljbffr

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