Postdoctoral Appointee - AI Co-Scientists

Postdoctoral Appointee - AI Co-Scientists

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We are seeking a highly motivated Post‑Doctoral Researcher to develop, implement, and advance the IDeA (Intelligent Design and Analysis) co‑scientist project. The successful candidate will work on cutting‑edge research at the intersection of artificial intelligence, agentic systems, and biological discovery.

You will be part of an initiative aimed at creating AI‑powered autonomous research assistants capable of hypothesis generation, experimental design, data analysis, and iterative learning in biological contexts. This project seeks to transform how computational and experimental biology research is conducted by developing intelligent systems that can function as collaborative partners in the scientific process.

Our group is pioneering the development of (1) generative AI models and agentic architectures for scientific reasoning, (2) scalable tool integration frameworks that connect AI systems with bioinformatics pipelines, computational modeling tools, and experimental platforms, and (3) feedback loops that enable continuous learning from biological data. As part of this team, you will gain hands‑on experience developing state‑of‑the‑art AI systems while contributing to transformative applications in biological discovery and translational research.

As a collaborative effort between Argonne and Oak Ridge National Laboratories, the candidate will have a unique opportunity to advance research in protein and enzyme engineering workflows on their respective leadership computing platforms.

• Design and implement generative AI models and agentic systems capable of scientific reasoning and hypothesis generation in biological contexts.
• Develop and integrate tool‑calling frameworks that enable AI agents to interact with bioinformatics software, computational modeling platforms, databases, and experimental systems.
• Build scalable architectures for multi‑agent systems that can coordinate complex research workflows across computational and experimental domains.
• Create feedback mechanisms that allow AI co‑scientists to learn iteratively from experimental results, simulation outputs, and literature.
• Implement prompt engineering strategies, retrieval‑augmented generation (RAG), and knowledge graph integration for scientific reasoning.
• Develop evaluation frameworks and benchmarks to assess the performance of AI co‑scientists in biological discovery tasks.
• Collaborate with experimental biologists, computational scientists, and engineers to identify use cases and validate AI‑driven discoveries.
• Optimize system performance for deployment on high‑performance computing infrastructure and cloud platforms.
• Publish findings in high‑impact journals and present research at leading AI and computational biology conferences.
• Contribute to open‑source tools and frameworks that advance the broader AI‑for‑science community.

• Work at the forefront of AI‑for‑science, developing autonomous systems that will transform biological research.
• Access to state‑of‑the‑art computing infrastructure, AI models, and collaborative research networks.
• Opportunity to shape the future of human‑AI collaboration in scientific discovery.
• Collaborate with leading researchers across AI, computational biology, and experimental sciences.
• Contribute to high‑impact publications and open‑source projects that will define the field.
• Competitive salary, professional development opportunities, and funding for conference travel.
• Engage with a vibrant, interdisciplinary research community dedicated to pushing boundaries.

• Completed or soon‑to‑be‑completed Ph.D. within the last 0–5 years in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Bioinformatics, or a related field.
• Strong programming skills in Python, with experience in AI/ML frameworks (PyTorch, JAX, Hugging Face Transformers).
• Experience developing or working with large language models (LLMs), agentic systems, or autonomous AI agents.
• Demonstrated ability to integrate AI systems with external tools, APIs, databases, or software packages.
• Understanding…