CHEManager International · 1 day ago
Postdoctoral Research Associate, Agentic Workflows
The Oak Ridge National Laboratory (ORNL) is seeking a dynamic Research Associate to focus on innovations in AI-integrated workflow architectures. The role involves advancing intelligent workflows for autonomous discovery and complex data integration using AI agents, contributing to scientific progress across various domains.
Newspapers
Responsibilities
Research and prototype LLM-driven agents capable of autonomous and interactive decision-making, anomaly detection, and guided experimentation in distributed scientific workflows
Design scalable systems for multi-workflow provenance capture, enhancing traceability, reproducibility, and transparency while facilitating intelligent multi-agent orchestration
Collaborate with domain scientists, computer scientists, engineers, and facility operators to integrate AI seamlessly into experimental and computational pipelines
Demonstrate the effectiveness of dynamic workflows in representative use cases such as materials discovery, combustion chemistry, and additive manufacturing
Publish research findings in peer-reviewed journals, conferences (e.g., SC, NeurIPS, AAAI), and open-source repositories
Mentor graduate students and contribute technical expertise to team projects aligned with ORNL's strategic scientific goals
Qualification
Required
Ph.D. in Computer Science, Data Science, Computational Science, or a relevant domain discipline (completed within the last 5 years or nearing completion)
Experience with scientific workflows, distributed systems, or AI agent development, particularly integrating LLMs or autonomous tools within complex pipelines
Proficiency in modern AI frameworks and tools (e.g., PyTorch, TensorFlow, LangChain, MCP SDKs) and programming languages (Python, C++)
Experience with provenance systems (e.g., Flowcept, W3C PROV) and data streaming tools (Kafka, Redis, RabbitMQ)
Understanding of HPC workflow orchestration platforms such as Argo, CrewAI, Parsl, or RADICAL-Pilot
Preferred
Knowledge of tools such as Grafana, Polars, or Pandas for monitoring and analyzing large-scale workflow execution and provenance data
Familiarity with synthetic workflows, graph-based reasoning, or computational chemistry/molecular dynamics workflows
Expertise in AI techniques such as retrieval-augmented generation (RAG), schema-driven reasoning, and graph traversal in provenance
Background in developing scalable tools for cross-domain, edge-to-HPC workflows using distributed architectures
Proven ability to integrate dynamic schema design and metadata enrichment into AI workflow systems
Company
CHEManager International
Wiley’s leading media brand providing first-hand information on the global chemical, life science and process industries
Funding
Current Stage
Growth StageCompany data provided by crunchbase