The Norwegian University of Science and Technology (NTNU) is inviting applications for a fully funded PhD Candidate position in Large Language Models (LLMs) for Data Extraction and Reasoning in Materials Science. This PhD fellowship offers an exceptional opportunity to conduct interdisciplinary research at the intersection of Artificial Intelligence (AI), Natural Language Processing (NLP), and Computational Materials Science.
Position Overview
| Field | Details |
|---|---|
| Position / Designation | PhD Candidate / PhD Research Fellow |
| Host Institution | Norwegian University of Science and Technology (NTNU) |
| Research Area | Large Language Models (LLMs), AI, Data Extraction, Materials Science |
| Location | Trondheim, Norway |
| Employment Type | Full-time (Standard 3-year or 4-year fully funded position) |
| Application Deadline | Check official portal (Jobbnorge ID: 307723) |
Designation
PhD Research Fellow / PhD Candidate in Computer Science & Materials Science Informatics.
Research Area & Project Focus
This research project focuses on advancing artificial intelligence methods, particularly modern Generative AI and Large Language Models (LLMs), to automate data extraction, knowledge synthesis, and structured reasoning from scientific literature and complex datasets within the field of materials science.
Key research topics include:
- Developing tailored LLM pipelines and NLP architectures for extracting materials properties, synthesis recipes, and characterization data from scientific papers.
- Investigating multi-step reasoning, Retrieval-Augmented Generation (RAG), and domain-adapted foundational models.
- Constructing domain knowledge graphs to bridge unstructured textual data with structured materials databases.
- Benchmarking and validating AI-driven scientific hypothesis generation in materials discovery.
Location
NTNU Campus, Trondheim, Norway.
Eligibility & Qualifications
Applicants must meet the formal requirements for admission to the PhD program at NTNU:
- Educational Background: A master’s degree (equivalent to 120 ECTS) in Computer Science, Artificial Intelligence, Data Science, Computational Materials Science, Physics, Informatics, or a closely related quantitative field.
- Academic Record: A strong academic background with an average grade of B or better (according to NTNU’s grading scale) in both your Bachelor’s and Master’s degrees.
- Technical Skills: Proficient programming skills in Python and deep familiarity with machine learning and NLP frameworks (e.g., PyTorch, Hugging Face, LangChain, Transformers).
- Language Proficiency: Excellent oral and written communication skills in English.
- Desirable Qualifications: Prior experience in materials informatics, scientific literature mining, or publications in relevant AI/NLP/Materials venues is considered a strong plus.
Scholarship & Position Description
PhD positions at NTNU are fully salaried government positions adhering to Norwegian labor standards. The selected candidate will receive:
- A competitive salary according to the Norwegian State salary scale for PhD Candidates.
- Full social security coverage, including pension scheme contributions (Norwegian Public Service Pension Fund) and comprehensive public health benefits.
- Dedicated funding for research travel, attending international conferences, and specialized training.
- Access to high-performance computing clusters and state-of-the-art AI infrastructure.
- An inclusive, collaborative international research environment with strong interdisciplinary ties.
How to Apply
Applications must be submitted electronically via the official Jobbnorge recruitment portal. Please prepare the following documents (in English, compiled into PDF format):
- Cover Letter / Research Statement: Explaining your motivation, background, and research interests related to LLMs and materials science.
- Curriculum Vitae (CV): Highlighting education, technical projects, publications, and relevant work experience.
- Academic Transcripts & Diplomas: Official transcripts and degree certificates for both Bachelor’s and Master’s programs.
- Master’s Thesis: An electronic copy of your Master’s thesis or an equivalent major scientific report/publication.
- References: Contact details of at least two academic or professional references.
Last Date for Apply
Until position filled
Official Application Link
To view the full vacancy details and submit your application, please visit the official job announcement:
Apply Online via Jobbnorge (Job ID: 307723)
