Postdoctoral Fellow in Computational Intelligence for Materials Informatics
Develop advanced AI-driven knowledge graphs and fine-tune large language models for materials science applications.
Best for:Best for postdoctoral researchers with expertise in deep learning, PyTorch, and knowledge graph construction, particularly those interested in applying AI to materials science and informatics.
Overview
This postdoctoral position at Toyota Technological Institute’s Computational Intelligence Lab focuses on integrating deep learning and natural language processing to advance materials informatics. The successful candidate will work on fine-tuning large language models (e.g., Llama) with domain-specific knowledge graphs to create high-performance information extraction systems for material science. The project emphasizes hands-on research, publication of findings, and collaboration within a multidisciplinary team. Applicants should have a strong background in computational methods and a passion for bridging AI with materials engineering.
What you'll do
- Develop and fine-tune large language models (e.g., Llama) using PyTorch to incorporate domain-specific knowledge graphs for materials science.
- Construct knowledge graphs representing material science knowledge and integrate them into AI models for enhanced information extraction.
- Conduct research on deep learning models for time-series prediction, natural language processing, and knowledge representation in materials informatics.
- Publish research findings in international conferences and journals, ensuring high academic impact.
- Collaborate with the Computational Intelligence Lab to advance interdisciplinary research at the intersection of AI and materials engineering.
- Dedicate full effort to the project, focusing on research outcomes and innovation in the field.
Requirements
Degree required: Doctorate in Engineering, Computer Science, or a related quantitative field (PhD in Engineering or equivalent, to be obtained by the start date)
Language: English (proficiency required for research and communication)
Instruction: English
Publications: Publication of research findings in international conferences or journals is expected.
- Proven experience in deep learning, particularly with large language models (LLMs) and fine-tuning techniques (e.g., LoRA, full/fine-tuning).
- Proficiency in PyTorch for model development and experimentation.
- Experience with knowledge graph construction and representation learning.
- Background in materials science or informatics, with a strong understanding of domain-specific knowledge.
- Experience publishing research in top-tier conferences or journals.
- No specific nationality or residency restrictions mentioned.
- Applicants must hold or be close to obtaining a PhD in Engineering or a related field by the start date.
Funding & terms
How to apply
Deadline: 2026-10-31
Applicants must submit a single PDF or ZIP file containing a CV with photo, a list of achievements, up to three key papers (PDF), a summary of past research (one A4 page), a research plan for the position (one A4 page), and one letter of recommendation. Submit via email to yutaka.sasaki@toyota-ti.ac.jp with the subject line 'PD（知能数理研究室）応募書類'. Password-protected files must have the password shared separately via email.
- CV with photo
- List of achievements
- Up to three key papers (PDF)
- Summary of past research (one A4 page)
- Research plan for the position (one A4 page)
- One letter of recommendation (including recommender’s name, affiliation, and contact details)
Contact
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