Specially-appointed researcher

Architecture Researcher Profile: Recognised Researcher (R2) Application Deadline: 25 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] * Background of the recruitment and description of the project Job Description The Center for Environmental Remote Sensing (CEReS), Chiba University, processes, archives, and publishes satellite data and related ground observation data, and conducts a broad range of research in Earth environmental sciences. At this time, we are recruiting a specially-appointed researcher to conduct education and research related to the development of AI-based subseasonal-to-seasonal prediction models. https://www.jst.go.jp/k-program/program/kaiyou4.html Work content and job description The successful candidate will conduct education and research on "AI-based seasonal prediction models" within a research project funded by the Japan Science and Technology Agency (JST), and will develop methods to improve 10-90-day subseasonal-to-seasonal prediction for societal applications, including ship operations. Building on systems such as ClimaX-LETKF and Anemoi-based global and regional graph neural networks, the candidate will develop AI seasonal prediction models that maintain numerical stability and physical consistency in integrations extending up to three months. The candidate will also develop AI models trained jointly on atmospheric and oceanic data, with the aim of establishing prediction methods that appropriately represent physical processes that underpin seasonal predictability and their remote influences. Specific research tasks may include the following. Development of AI weather and climate models, including subseasonal-to-seasonal prediction models based on the existing ClimaX-LETKF or Anemoi system Development of AI prediction models capable of stable integration with longer forecast intervals Development of forecast-correction methods using conditional diffusion models, generative AI, statistical correction, and related approaches Development of Transformer-based super-resolution methods for low-resolution outputs from physical and AI models This position is intended not merely to apply existing AI models, but to advance research that integrates AI prediction models, generative models, data assimilation, and ensemble forecasting while accounting for the physical processes governing predictability on subseasonal-to-seasonal timescales. Please contact Prof. Shunji Kotsuki below for further details. * Assigned department Existing departments Work location * Address 263-8522 Chiba 1-33 Yayoi-cho, Inage-ku, Chiba-city, Chiba, Japan Number of hired: 1 person(s) Hiring date: 2026-12-01 00:00:00 Starting date: 2026-12-01 00:00:00 Job content supplemental explanation: As early as possible between 1st December 2026 and 1st April 2027 (negotiable). Where to apply Website: https://jrecin.jst.go.jp/seek/SeekJorDetail?fn=0&ln=1&id=D126110287&ln_jor=1 Additional Information Benefits [Compensation] Annual salary: 5 million yen - 6 million yen Wages description: Salary will follow university regulations and depend on years of experience. For a researcher in the first year after obtaining a Ph.D., the annual salary is expected to be around 5.50 million JPY. Salary increases may be possible depending on achievements. Working hours: 08:30-17:15 Break time: 12:00-13:00 Holidays: Saturdays, Sundays, National holidays, and year-end and New Year holidays. Overtime and other explanations: under the Discretionary Labor System for Professional Work Employment type: Contract employee Employment type description: Specially-appointed researcher (full-time) Contract period: Nontenured Contract period description: The contract is annual (until March 31 of each fiscal year). Renewal may be possible depending on performance, research needs, and available funding. Probationary period: Probationary period absent [Various systems] Transportation expenses: yes [Insurance] Employees' Health Insurance: yes Employees' Pension Insurance: yes Worker's accident insurance: yes Employment insurance: yes [Measures for preventing passive smoking at the workplace] no smoking on campus Supplementary explanation of compensation Employee Benefits: (1) Job title: Specially-appointed researcher (full-time). (2) Salary, etc.: University's Specified Employment Staff Salary Regulations based on your years of experience will be applied (3) Working hours: from 8:30 to 17:15, 5 days a week, 7 hours and 45 minutes per day (under the Discretionary Labor System for Professional Work). (4) Holidays: Saturdays, Sundays, National holidays, and year-end and New Year holidays. (5) Leave: granted based on the University's employment agreement. (6) Various allowances: provide commuting allowance if requirements are met based on the regulations. (7) Insurance: join a mutual aid association, employment insurance, and workers' accident compensation insurance Additional comments Application documents will not be used for any other purposes than the selection process. These documents will not be returned unless a stamped self-addressed envelope is enclosed. Interviews may be conducted during the selection process. Travel expenses will not be reimbursed. Salary will be determined in accordance with Chiba University's regulations for specially appointed staff, based on years of experience. For a researcher in the first year after obtaining a Ph.D. degree, the expected annual income is approximately 5.5 million yen. The laboratory has established a Research Policy concerning authorship, research activities, working conditions, and the research environment. Work Location(s) Number of offers available: 1 Company/Institute: Chiba University Country: Japan City: Chiba Contact City: Chiba Please contact Prof. Shunji Kotsuki for further details.