Young Scientists, AI Engineers, and Postdoctoral Fellows

Agricultural University. Drawing on the strategic scientific and technological capabilities of Yazhouwan National Laboratory, the disciplinary strengths of Huazhong Agricultural University, and the industrial resources of leading enterprises such as HUAWEI and Baidu, the College aims to become a leading center for innovation, talent development, and industrial advancement in agricultural artificial intelligence, thereby contributing new momentum to national food security and the digital transformation of agriculture. We warmly welcome outstanding talents from around the world to join the College of Artificial Intelligence and work with us to shape the future of smart agriculture. II. Positions and Requirements A. Young Scientists: 5 Positions Research Area 1: Scientist in Bio-breeding Algorithms Develop intelligent decision-making algorithms and models for biological breeding. Research Area 2: Scientist in Agricultural Sensor R&D Design and develop intelligent agricultural sensors. Research Area 3: Scientist in Phenomics Intelligent Equipment R&D Design and develop intelligent equipment for plant phenomics. B. AI Engineers: 5 Positions Responsible for extracting and managing various types of corpus data and machine-generated data to ensure a stable data supply for large language models. Develop data processing toolchains for data collection, cleaning, annotation, desensitization, and storage, as well as design and develop LLM-oriented fine-tuning workflows and prompt templates. Design and implement AI algorithms and systems, including but not limited to neural networks and machine learning, computer vision, intelligent decision-making, recommender systems, large language models, and generative AI. Track and analyze the latest trends in AI, including but not limited to large language models, generative AI, and AI for coding. C. Postdoctoral Fellows: 10 Positions Research Area 1: Postdoctoral Fellow in Phenomics Develop and integrate next-generation plant phenotyping platforms, with a focus on multi-sensor data fusion and automated control systems. Build high-throughput and unmanned phenotypic data acquisition platforms to enable precise and dynamic measurement of key traits from indoor pot experiments to field-scale scenarios. Develop data processing and analysis algorithms compatible with phenotyping hardware, transforming raw data into biologically meaningful trait indicators and providing core data support for downstream breeding decisions. Research Area 2: Postdoctoral Fellow in Plant Science Conduct precise identification and evaluation of plant germplasm resources, and carry out high-throughput phenotypic data acquisition and analysis for key traits. Participate deeply in multi-omics research, such as phenomics and spatiotemporal omics, and contribute to the interpretation and experimental validation of biological phenomena. Work closely with bioinformatics and geospatial information teams to provide robust biological data and theoretical support for the “digital design breeding” platform. Research Area 3: Postdoctoral Fellow in Bioinformatics Integrate, mine, and analyze multi-omics data, including genomic and phenomic data. Develop or apply bioinformatics tools and statistical models to identify key genes and pathways regulating important agronomic traits. Construct and optimize genotype–phenotype association models, providing algorithmic and data-analysis support for intelligent breeding decision-making. Research Area 4: Postdoctoral Fellow in Geospatial Information Science Collect, process, and analyze breeding-related environmental data, including meteorological data, soil data, and remote sensing imagery. Investigate the interactions between crop growth and environmental factors, and develop geospatial information-based models for environmental effects. Support the development of multidimensional “genotype–environment–phenotype” decision-making models, providing spatial decision support for variety design and deployment across different ecological regions. III. Team Briefing Team Introduction The team focuses on digital design breeding by integrating phenomics, spatiotemporal omics, multi-omics technologies, high-throughput phenotyping, genotyping, and germplasm identification. By establishing standardized coupled analyses of phenotypic, molecular, physiological, and environmental data, the team aims to enable digital simulation and precision-oriented evaluation for seed design and creation. The team is developing a comprehensive technical support system for high-throughput genotyping, molecular identity profiling of new varieties, intellectual property protection in agricultural biological breeding, and multi-location testing and integrated evaluation of new varieties. By combining precise genome manipulation, plant–microbe–environment interactions, and data intelligence, the team is building a high-throughput “digital design breeding” platform based on panoramic seed data. This platform will advance intelligent decision-making breeding technologies driven by the integration of biotechnology, data technology, and artificial intelligence, namely “BT + DT + AI.” With strong high-performance computing capacity and state-of-the-art research facilities, the team provides an excellent interdisciplinary research environment for scientists, postdoctoral fellows, and engineers working at the interface of biological breeding, data science, and artificial intelligence. Research Focus 1: AI-enabled Agricultural Phenomics (Wanneng Yang) Develop key technologies for high-throughput and high-precision extraction and analysis of full spatiotemporal phenomes of plants and animals across multiple biological scales, including cells, organs, individuals, and populations. Develop AI-based methods for multimodal and multi-omics data fusion, interpretation, and biological discovery. Design and develop intelligent high-throughput phenotyping equipment for plants and animals. Establish standardized systems for phenotypic data acquisition, management, annotation, and sharing. Build application scenarios for intelligent phenomics in breeding, cultivation, evaluation, and agricultural production. Research Focus 2: Agricultural Robotics (Peng Song) Develop unmanned robotic systems for crop phenotyping and field-based trait detection. Develop precision mechanized equipment for the full cycle of field crop production, including tillage, sowing, crop management, and harvesting, such as intelligent seeders and variable-rate fertilizer applicators. Develop harvesting robots, plant protection robots, inspection robots for controlled-environment agriculture, and compact intelligent agricultural machinery for hilly and mountainous regions. Develop key technologies in autonomous navigation, positioning, path planning, and obstacle avoidance for agricultural robotic systems. Research Focus 3: Agricultural Big Data and Intelligent Decision-Making (Jian Zhang) Develop multi-source phenotypic data acquisition and analysis equipment and platforms integrating spaceborne, airborne, and ground-based sensing systems. Support breeding decision-making through high-throughput field phenotyping and intelligent data analytics. Develop regional monitoring-driven models for evaluating variety performance, growth dynamics, and agronomic regulation strategies. Formulate future climate-driven breeding objectives and strategic decision-making frameworks. Research Focus 4: AI-enabled Breeding (Lin Li) Develop intelligent management systems for crop germplasm resources. Optimize intelligent algorithms for precision design breeding. Conduct intelligent analysis of multi-environment breeding trial data and mine genotype–phenotype associations. Perform genome-wide identification of functional genes and precise evaluation of their genetic effects. Research Focus 5: Intelligent Agricultural Sensors (Peiwen Liu) Develop innovative flexible sensing materials and regulate their functional properties. Design and manufacture high-performance sensor devices tailored to agricultural applications. Integrate and apply intelligent sensing systems for multi-source agricultural information acquisition. Research Focus 6: Future Plant Systems for Agriculture (Rongbo Zhu) Develop spatiotemporally modulated multi-band LED lighting technologies and intelligent light-recipe generation systems. Develop in situ rapid soil testing technologies and multi-factor on-demand nutrient and resource supply regulation methods. Develop multimodal, non-destructive phenotypic sensing technologies and predictive models for coordinated organ-level demand in plant growth. Research Focus 7: AI-enabled Agricultural Materials (Qiang Li) Develop intelligent algorithms for agricultural materials genomics, including knowledge-embedded graph neural networks and inverse design frameworks that integrate physical mechanisms with materials-genome features. Build a high-throughput robotic experimentation platform integrating “design–fabrication–sensing” for rapid agricultural material creation. Establish dynamic association models between process parameters and real-time material states to enable intelligent closed-loop control of material preparation processes. Research Focus 8: Intelligent Animal Breeding and Precision Livestock Health Management (Xiaolei Liu) Develop holistic multimodal breeding technology systems for agricultural animals. Build multidimensional animal omics knowledge bases and elucidate the genetic mechanisms underlying important traits. Develop intelligent diagnosis and early-warning technologies for major animal infectious diseases and health risks. Research Focus 9: Smart Horticulture (Qiang Xu) Develop genomic resources and breeding technologies for horticultural crops, including the construction of citrus genome databases, citrus variety identification systems, and DNA fingerprinting databases for citrus germplasm. Investigate vegetative propagation and grafting biology, including the identification of key genes controlling apomictic polyembryony and the construction of their regulatory networks, as well as the mechanisms of interaction and signal transmission between rootstocks and scions. Identify and utilize genes associated with resistance to Huanglongbing, also known as citrus greening disease, by mining genetic resources from citrus wild relatives and HLB-tolerant germplasm. Explore the cross-regulatory networks between disease resistance and fruit quality for resistance improvement. Investigate the regulation and genetic improvement of fruit quality by identifying genes controlling fruit color, flavor, and vitamin C content, and applying these genes to horticultural breeding. IV. Support and Benefits Career Development: Successful candidate will be appointed as a professor or research fellow, incorporated into the university’s talent support system. Remuneration and Benefits: Competitive salary, research funding, graduate student quotas, and excellent working conditions will be negotiated on an individual basis with the candidate. Team-based employment will be provided with additional startup funding and resource support. Eligible candidates may apply for named faculty positions, with additional competitive stipends provided by the university’s alumni foundation during the term. V. How to Apply Qualified candidates are requested to submit the following materials by email to rcb@mail.hzau.edu.cn : CV, degree certificates, professional title certificates, proof of employment, and evidence of major achievements. VI. Contact Information Contact: Chang Liu Tel: +86-027-87280957 Email: rcb@hzau.edu.cn