Jianbing Jin is an associate professor at Nanjing University of Information Science and Technology. He obtained his Ph.D. in 2019 from Delft University of Technology in the Netherlands, majoring in Applied Mathematics, under the supervision of Prof. Hai Xiang Lin and Prof. Arnold Heemink. In 2019, he joined TNO (Netherlands Organization for Applied Scientific Research) as an Assistant Scientist, collaborating with Dr. Arjo Segers and Prof. Bas Henzing. In 2020, he joined Nanjing University of Information Science and Technology, collaborating with Prof. Hong Liao.His current research interests focus on the theory and application of data assimilation (integration of numerical models and observational data), machine/deep learning applications in the Earth system, and reprocessing of atmospheric composition satellite observation data. He has published more than 10 academic papers in journals such as Geoscientific Model Development and Atmospheric Chemistry and Physics.In terms of research collaboration and student recruitment, he emphasizes cutting-edge research in Earth sciences, using data assimilation, deep learning, and numerical models as tools. The principle is to publish high-quality papers, explore meaningful and interesting research topics, and achieve impactful research results. Requirements for students include a certain level of programming skills (Python, Fortran, C, etc.) and a strong foundation in mathematics. Students are expected to have a high level of self-motivation, strong willpower, and a spirit of mutual assistance and cooperation among peers.He is committed to adopting a rigorous academic training model from top international universities to cultivate top-notch research talents. He maintains one-on-one weekly meetings with each student to discuss academic progress. For outstanding graduate students, competitive scholarships will be provided for short-term visits abroad. Additionally, students aspiring to pursue a Ph.D. will have the opportunity to be recommended to prestigious institutions in Europe and the United States.Recent First Author and Corresponding Author* Publications:(15) Bufan Xu, Jianbing Jin*, Li Fang, Mijie Pang, Ji Xia, Baojie Li, Hong Liao*. A decadal atmospheric ammonia reanalysis product in China. Science of the Total Environment. 912, 169053, 2024.(14) Li Fang, Jianbing Jin*, Arjo Segers, Hong Liao*, Ke Li, Bufan Xu, Wei Han, Mijie Pang, Hai Xiang Lin. A gridded air quality forecast through fusing site-available predictions from RFSML v1.0 and chemical transport model results from Geos-Chem v13.1.0 using the ensemble Kalman filter. Geoscientific Model Development, 16, 4867-4882, 2023.(13) Mijie Pang, Jianbing Jin*, Arjo Segers, Huiya Jiang, Li Fang, Hai Xiang Lin, Hong Liao. Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter. Atmospheric Environment, 119831, 2023.(12) Jianbing Jin, Li Fang, Baojie Li, Hong Liao*, Ye Wang, Wei Han, Ke Li, Mijie Pang, Xingyi Wu, Hai Xiang Lin. 4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals. Environmental Research Letters. acb835, 2023.(11) Jianbing Jin, Bas Henzing*, Arjo Segers. How aerosol size matters in AOD assimilation and the optimization using the Ångström exponent. Atmospheric Chemistry and Physics. 23, 1641-1660, 2023.(10) Li Fang, Jianbing Jin*, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao*. Development of a regional feature selection-based machine learning system (RFSML v1.0) for air quality forecasting over China. Geoscientific Model Development, 2022, 15, 1-17.(9) Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao*. Inverse modeling of the 2021 spring super dust storms in East Asia. Atmospheric Chemistry and Physics, 2022, 22, 6393-6410.(8) Cong Xiao, Jianbing Jin*. Model-reduced Adjoint-based Inversion using Deep Learning. Water Resources Research. 57, 2022.(7) Jianbing Jin*; Arjo Segers; Hai Xiang Lin; Bas Henzing; Xiaohui Wang; Arnold Heemink; Hong Liao*. Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0. Geoscientific Model Development. 14, 5607–5622, 2021.(6) Min Xu; Jianbing Jin*; Guoqiang Wang; Arjo Segers; Hai Xiang Lin. Machine learning based bias correction for numerical chemical transport models. Atmospheric Environment, 2021, 248: 118022(5) Jianbing Jin*; Arjo Segers; Hong Liao*; Arnold Heemink; Richard Kranenburg; Hai Xiang Lin. Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China. Atmospheric Chemistry and Physics, 2020, 20: 15207-15225(4) Jianbing Jin. Dust Storm Emission Inversion using Data Assimilation. Delft University of Technology, 2019 (academic monograph).(3) Jianbing Jin*; Hai Xiang Lin; Arjo Segers; Yu Xie; Arnold Heemink. Machine learning for observation bias correction with application to dust storm data assimilation. Atmospheric Chemistry and Physics, 2019, 19(15): 10009-10026.(2) Jianbing Jin*; Arjo Segers; Arnold Heemink; Mayumi Yoshida; Wei Han; Hai Xiang Lin. Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017. Journal of Advances in Modeling Earth Systems, 2019, 11(2): 446-467.(1) Jianbing Jin*; Hai Xiang Lin; Arnold Heemink; Arjo Segers. Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar. Atmospheric Environment, 2018, 187: 358-373.Recent Research Projects:2023.01-2025.12 National Key Research and Development Program Government-Industry International Technology Innovation Cooperation Project (2022YFE0136100), Subproject Leader.2022.07-2025.06 Jiangsu Province Carbon Peak and Carbon Neutrality Science and Technology Innovation Special Fund Project (BK20220031), Subproject Leader.2022.01-2024.12 National Natural Science Foundation of China Youth Project (42105109), Principal Investigator.2021.07-2024.06 Jiangsu Province Youth Fund (BK20210664), Principal Investigator.