Job Information
Organisation/Company: Tokyo University of Science Department: Division of Molecular Biology of Aging, The Research Institute for Science and Technology (RIST) Research Field: Computer science, Engineering, Information science, Technology Researcher Profile: Recognised Researcher (R2) Application Deadline: 31 Dec 2026 - 23:59 (UTC) Country: Japan Type of Contract: Permanent Job Status: Not Applicable Hours Per Week: See description Is the job funded through the EU Research Framework Programme?:…
Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?: No
Offer Description
[Background of the recruitment and description of the project]
Aging does not progress uniformly throughout the body. The brain, immune system, cardiovascular system, liver, and other organs age at different rates. Because circulating proteins reflect the physiological states of multiple organs and cell types, combining plasma proteomics with artificial intelligence may enable the minimally invasive assessment of organ-specific biological age and functional decline.
This project aims to develop organ-age and organ-age-gap prediction algorithms applicable to Asian populations by integrating large-scale plasma proteomic datasets generated using the Olink and SomaScan platforms. The organ age gap is defined as the difference between an organ’s AI-predicted biological age and an individual’s chronological age. It provides a quantitative measure of whether a particular organ is aging faster or slower than expected.
Existing organ-age models have largely been developed using Western cohorts and specific proteomic platforms. Their reproducibility in Asian populations and their transferability between Olink and SomaScan therefore remain insufficiently established.
We will apply regularized regression, gradient boosting, domain calibration, and explainable AI approaches such as SHAP to construct models that are robust to population and measurement-platform differences. Proteins contributing to organ-age predictions will subsequently be linked to aging-related pathways, including inflammation, mitochondrial dysfunction, cellular senescence, chromatin regulation, and extracellular-matrix remodeling.
The long-term objective is to establish a research foundation for visualizing biological aging, evaluating health-related risks, identifying potential intervention targets, and ultimately developing molecular digital twins that represent individual biological states.
Work content and job description
The successful candidate will work closely with the principal investigator and collaborating researchers and will contribute to the following activities:
- Data organization, quality control, and preprocessing of plasma proteomic datasets, primarily generated using Olink and SomaScan
- Harmonization of protein identifiers, measurement units, missing values, demographic variables, and sample-collection metadata
- Cross-platform protein mapping and calibration between Olink and SomaScan
- Annotation of organ-enriched proteins using GTEx and other public biological resources
- Development of organ-age prediction models using LASSO, Elastic Net, gradient boosting, and related machine-learning approaches
- Evaluation of model generalizability using train/test splits, leave-one-cohort-out validation, and leave-one-platform-out validation
- Assessment of predictive performance using MAE, R², intraclass correlation, rank correlation, and calibration metrics
- Identification of organ-specific contributing proteins using SHAP values, model coefficients, and network-based analyses
- Integration of predictive proteins with molecular signatures related to aging, inflammation, mitochondrial function, chromatin regulation, and extracellular-matrix remodeling
- Biological interpretation using ICE mouse data, single-cell datasets, and publicly available transcriptomic resources
- Development and documentation of reproducible analytical pipelines, metadata specifications, and evaluation protocols
- Preparation of data visualizations, research reports, conference presentations, and scientific manuscripts
- Participation in project meetings and interdisciplinary collaboration with life scientists and data scientists
This position offers an opportunity to work at the interface of aging biology, proteomics, and artificial intelligence. The successful candidate will address fundamental questions such as how organ aging can be quantified, how data from different proteomic technologies can be integrated, and how pro
Assigned department
Existing departments
Work location
Address: 278-8510 Chiba 3rd Floor, Building 10, 2641 Yamazaki, Noda-shi,
Number of hired
Number of hired:2 person(s)
Requirements
Additional Information
Benefits
Annual salary:4 million yen - 7 million yen
Working hours:09:00-18:00
Break time:12:00-13:00
Employment type:Regular employee
Contract period:Nontenured
Contract period description:1 year. Contract renewed in April of each fiscal year.
Probationary period:Probationary period present
Probationary period description:3 months
Various systems
Pay increase system:available
Transportation expenses:available
Insurance
Employees' Health Insurance:available
Employees' Pension Insurance:available
Worker's accident insurance:available
Employment insurance:available
Measures for preventing passive smoking at the workplace
Supplementary explanation of compensation
Work Location(s)
Number of offers available: 1
Company/Institute: Tokyo University of Science
Country: Japan
City: Chiba
Contact
City: Tokyo
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