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source

RIKEN

Just Landed

Researcher or Postdoctoral Fellow in Materials AI Foundational Model Research RIKEN in Japan

Degree Level

Postdoc

Field of study

Computer Science

Funding

Annual income starts at 4 million yen and is determined through an annual salary system based on experience, skills, and achievements, consisting of fixed and variable components. The monthly salary and discretionary-work allowance at hiring total at least 536,065 yen for researchers and 382,610 yen for special researchers. Commuting and housing allowances are provided, along with social insurance and other employment benefits.

Deadline

Nov 13, 2027

Country flag

Country

Japan

University

RIKEN

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Keywords

Computer Science
Chemistry
Information Technology
Materials Science
Spectroscopy
Artificial Intelligence
Computational Science
X-ray Diffraction
Physics

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About this position

RIKEN’s Center for Computational Science is recruiting a researcher or postdoctoral-equivalent special researcher for the AI for Science Foundational Model Research Team. The position contributes to a JST Advanced AI Science Research Promotion Program project developing a general-purpose materials science platform model.

The project focuses on multimodal foundation models that can support scientific research activities such as research proposal generation, experimental design, program development, experiments and simulations, and analysis of results. The successful researcher will help determine how materials measurement data should be represented so that it can be learned by AI models.

Responsibilities include defining data specifications, normalization methods, and invariant handling for modalities such as X-ray diffraction, XPS, XAS, Raman and infrared spectroscopy, NMR, EELS, thermal analysis, electron microscopy, composition tables, synthesis and process conditions, and metadata. The role also involves designing and quality-controlling training data drawn from ARIM data, the literature, experimental records, and materials databases.

The researcher will design representations of process–structure–physical property–performance relationships and develop evaluation benchmarks for materials-science tasks, including structure–property inference, physical-property prediction, and synthesis planning. Evaluating the physical validity of model outputs is a key part of the work. Model training, inference implementation, and distributed learning-platform development will be handled by other team members, allowing this position to focus on scientific representation, materials characterization, and evaluation.

The appointment is based in RIKEN’s AI for Science Platform Division within the Center for Computational Science in Tokyo, in collaboration with partner institutions including the National Institute for Materials Science and the Institute of Statistical Mathematics. The role offers an interdisciplinary research environment spanning materials informatics, analytical chemistry, materials characterization, artificial intelligence, and computational science.

Funding details

Annual income starts at 4 million yen and is determined through an annual salary system based on experience, skills, and achievements, consisting of fixed and variable components. The monthly salary and discretionary-work allowance at hiring total at least 536,065 yen for researchers and 382,610 yen for special researchers. Commuting and housing allowances are provided, along with social insurance and other employment benefits.

What's required

Applicants must hold a doctoral degree in a field related to the duties; special researcher applicants may include those expecting to obtain the doctorate by the start date and generally must have received the doctorate within five years. Applicants should have practical experience in materials measurement or characterization and be able to explain the physical meaning, quality, and limitations of measurements in at least one of diffraction, spectroscopy, thermal analysis, electron microscopy, or composition analysis. Data-oriented research experience is required, such as materials informatics, high-throughput or autonomous experimentation, materials database construction, or measurement-data analysis. Applicants must communicate and collaborate effectively with internal and external colleagues. Deep-learning or machine-learning implementation experience is not required; Python data-processing experience or experience applying machine learning to measurement data is desirable.

How to apply

Review the official RIKEN recruitment page and submit the required application documents, including a CV, publication record, and other documents specified there, through the RIKEN institutional application process. Applications are accepted until November 13, 2027, but recruitment may close earlier once a suitable candidate is selected. Inquiries may be sent to [email protected] or Rio Yokota at [email protected].

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