About the Carbon Cycle Lab

We are committed to advancing the understanding of forest carbon and biodiversity dynamics through cutting-edge science and technology.

  • Developing advanced upscaling methodologies by integrating multi-source data from field observations, flux towers, drones, and satellites;

  • Investigating forest succession, disturbance regimes, and ecosystem recovery processes;

  • Establishing process-guided AI models to simulate and predict carbon fluxes and biodiversity dynamics in forest ecosystems.

Our team contributes to regional sustainability by promoting carbon neutrality and biodiversity conservation.

Key Questions

We focus on providing insights into the three scientific questions: (1) How to quantify destinies of the carbon atoms fixed by photosynthesis? (2) How does forest ecosystem respond to environmental change? (3) How to establish a positive feedback between human and nature?

Recruitment News

  • 研究方向:
    生态系统“天-空-塔-地一体化”智能感知新范式
    Research Direction:
    Integrated Satellite-Drone-Tower-Ground Intelligent Sensing of Ecosystems

    研究内容:
    构建亚热带森林“天-空-塔-地一体化”智能观测平台，发展生态系统结构和功能智能监测和反演新范式。例如，利用超高分辨率无人机遥感监测平台，突破个体尺度植物物种大范围精准识别的技术瓶颈，实现受威胁植物的智能识别以及大尺度植物多样性制图；利用“空-地”结合的激光雷达观测技术，突破亚热带森林生物量估算的技术瓶颈，实现亚热带森林全三维结构建模以及地上生物量碳收支的精准核算。
    Research Focus:
    Establish an integrated satellite-drone-tower-ground intelligent observation platform for subtropical forests, and develop a new paradigm for intelligent monitoring and retrieval of ecosystem structure and function. For example, utilize an ultra-high-resolution drone remote sensing platform to overcome technical bottlenecks in large-scale, individual-level plant species identification, enabling intelligent detection of threatened species and large-scale biodiversity mapping; combine airborne and ground-based LiDAR technologies to surpass existing limitations in biomass estimation, achieving full 3D structural modeling and precise accounting of aboveground biomass carbon dynamics in subtropical forests.

    专业要求:
    具有遥感和计算机科学背景；具有深度学习和人工智能方面的研究基础。
    Preferred Background:
    Background in remote sensing and computer science; research experience in deep learning and artificial intelligence.

  • 研究方向:
    亚热带森林生态系统对全球变化的响应与适应
    Research Direction:
    Response and Adaptation of Subtropical Forest Ecosystems to Global Change

    研究内容:
    基于“天-空-塔-地”长时间序列观测数据，研究全球变化背景下，亚热带森林生态系统自然演替过程及其稳定性的维持机制，植物多样性的时空格局及驱动机制，受威胁植物的生境变化以及就地和迁地保护，生态系统结构与功能的响应和适应规律，生物和非生物扰动和恢复过程，以及多样性、生产力和植被土壤碳库之间的协同演化等。
    Research Focus:
    Based on long-term, integrated satellite-drone-tower-ground observations, this research field aims to investigate the processes of natural succession and the mechanisms maintaining the stability of subtropical forest ecosystems under global change. It will explore the spatiotemporal patterns and driving forces of plant biodiversity, habitat changes of threatened species and their in situ and ex situ conservation, the responses and adaptations of ecosystem structure and function, the processes of biological and abiotic disturbances and recovery, as well as the co-evolution among biodiversity, productivity, and vegetation-soil carbon pools.

    专业要求:
    具有生态学、地理学、遥感科学背景；具有野外调查经历；具有数理统计背景以及熟练的大数据处理和分析能力（例如，Google Earth Engine平台、机器学习和深度学习等）。
    Preferred Background:
    Background in ecology, geography, or remote sensing; fieldwork experience; strong skills in statistical analysis and large-scale data processing (e.g., Google Earth Engine, machine learning, deep learning).

  • 研究方向:
    亚热带森林生态系统过程模型发展与模拟预测
    Research Direction:
    Development of Process-Based Models for Subtropical Forest Ecosystems

    研究内容:
    基于已有且长期发展的生态系统模型架构（例如，ED-2、CLM5、CABLE等），构建专门针对亚热带森林生态系统的过程模型，重点考虑并耦合亚热带森林生态系统的自然演替规律、生物和非生物扰动因子、以及多样性和碳循环之间的耦合关系等关键缺失过程，利用最新的模型-数据同化技术对过程模型参数进行优化，进而更可靠地模拟和预测全球变化背景下区域森林植被多样性、生产力和碳储量的协同演化及提升潜力。
    Research Focus:
    Building on existing and long-developed ecosystem modeling frameworks (e.g., ED-2, CLM5, CABLE), this research field aims to develop a process-based model specifically for subtropical forest ecosystems. The model will explicitly incorporate and couple key missing processes, including natural succession dynamics, biological and abiotic disturbance regimes, and the interactions between biodiversity and the carbon cycle. By applying state-of-the-art model–data assimilation techniques to optimize model parameters, the goal is to more reliably simulate and predict the co-evolution and enhancement potential of forest biodiversity, productivity, and carbon stocks under global change.

    专业要求:
    具有生态学、地理学背景，具有较强的过程模型开发背景（例如，ED-2、CLM5、CABLE、ORCHIDEE、LPJ-GUESS、FATES等）；具有数理统计背景以及熟练的多模型数据（例如，TRENDY、CMIP6等）处理和分析能力。
    Preferred Background:
    Background in ecology or geography; strong experience in process-based model development (e.g., ED-2, CLM5, CABLE, ORCHIDEE, LPJ-GUESS, FATES); statistical background with proficiency in multi-model data processing and analysis (e.g., TRENDY, CMIP6 datasets).

  • 邮件主题请注明“应聘副研究员/助理研究员/博士后/博士/硕士-姓名-研究方向”）。
    Please specify in the email subject: "Application for Associate Prof./Assistant Prof./Postdoc/PhD/Master-Name-Research Field"

    针对每个方向，招聘长聘副研究员或助理研究员1–2名，博士后1–2名，博士或硕士研究生1–2名（本招聘长期有效，欢迎咨询）。
    For each research field, we are recruiting 1–2 tenured associate or assistant professors, 1–2 postdoctoral fellows, and 1–2 PhD or master's students (This position remains open; feel free to contact us.)

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