Recommended-Admission Master’s Openings

Now recruiting / 招生进行中

The PKU-EMBL Lab is recruiting recommended-admission master’s students in two complementary tracks: Computational & AI Research (Dry Lab) and Experimental Microbiology (Wet Lab). Both tracks offer a three-year academic master’s program and integrated computational-experimental training.

Lab Overview

The PKU-EMBL Lab studies microbial resources from extreme environments, including hypersaline, alkaline, acidic, and cold habitats. We are building a high-throughput pipeline that connects environmental microbial “dark matter” with computable knowledge and experimental validation.

The lab has conducted large-scale metagenomic and metatranscriptomic sequencing together with environmental physicochemical measurements across salt, acid, and alkaline lakes in northwestern China. These datasets account for more than 80% of comparable datasets worldwide, providing a distinctive foundation for training, validating, and translating AI models for microbiome research. Our broader goal is to establish an iterative AI prediction—experimental validation—model refinement workflow.

Computational & AI Track

Research Directions

  1. Functional genomics and biological foundation models: combine protein and genome language models with deep learning to identify low-abundance functional groups and discover novel enzymes, remote homologs, biosynthetic gene clusters, and environmental adaptation elements.
  2. Metabolic knowledge graphs and scientific agents: connect genes, enzymes, substrates, products, and reactions through knowledge graphs, vector databases, multi-omics retrieval, and multi-agent reasoning for automated pathway discovery and feasibility assessment.
  3. Microbial community world models: integrate metagenomes, metatranscriptomes, metabolite exchange, and environmental variables to predict community interactions, state transitions, and intervention responses.
  4. High-performance software and community Hub: develop bioinformatics algorithms, databases, automated workflows, and visualization platforms while exploring GPU acceleration and agent-driven research workflows.

Candidate Profile

Applicants should have enthusiasm for AI for Life Science, microbiome research, or computational biology; a strong foundation in mathematics, statistics, or computer science; and proficiency in at least one language such as Python, Rust, C, or C++. Experience in biological foundation models, scientific agents, bioinformatics, high-performance computing, or knowledge graphs is expected. Proficiency in Vibe Coding, reusable Skills, and other AI-assisted development workflows is strongly encouraged. Research projects, open-source work, algorithm competitions, and end-to-end software projects are valued but are not strict requirements.

Experimental Microbiology Track

Research Directions

  1. Cultivation and identification of extremophiles: develop efficient cultivation methods for difficult-to-culture and low-abundance microorganisms and build a distinctive extremophile resource collection.
  2. Validation of novel enzymes and functional genes: clone, express, and characterize AI-predicted enzymes, remote functional genes, and environmental adaptation elements.
  3. Heterologous expression of biosynthetic gene clusters: clone and assemble target clusters, establish expression systems, detect products, and investigate novel natural-product biosynthesis.
  4. Pathway characterization and synthetic biology: combine gene knockout, overexpression, CRISPR, and metabolomics to validate new pathways and optimize chassis and expression systems.
  5. Translation: advance functional strains, extremozymes, and natural products toward bioremediation, biomass and energy conversion, and pharmaceutical development.

Candidate Profile

Applicants should have strong molecular biology fundamentals and practical experience with molecular cloning, PCR, and protein expression and purification. Experience with heterologous expression in E. coli, yeast, or other systems is preferred. CRISPR, metabolic engineering, natural-product analysis, or metabolomics experience is a plus. We value strong hands-on ability, independent experimental planning, rigorous record-keeping, and willingness to use AI-assisted research tools and collaborate closely with computational researchers.

Program and Training

  • Program: three-year academic master’s degree (discipline code 07, Science)
  • Training model: integrated dry-wet training with a domestic and international mentoring team
  • Resources: H200, A100, and L40S GPU clusters; comprehensive experimental platforms; extensive extreme-environment samples and multi-omics datasets; and opportunities for academic exchange and industry translation
  • Internships: high-quality internships are encouraged and fully supported with flexible arrangements
  • Goal: develop interdisciplinary researchers with international perspective, independent research ability, and the capacity to move discoveries from data and models to experimental validation and application

How to Apply

Please email your CV to hwu202425@gmail.com and cc the lab supervisor at yuke.sz@pku.edu.cn. A research proposal is optional.

Please use the following format for both the email subject and attachment name:

Format:[保研申请] 姓名-学校-专业-喜欢的研究方向

You are also welcome to send a direct message introducing your background. We will respond to every inquiry.

中文招生信息

PKU-EMBL 课题组现招收计算与 AI（干实验）方向和实验微生物学（湿实验）方向推荐免试硕士研究生。两类方向均为 3 年学术型硕士（07 理学代码），实行干湿结合的交叉培养。

课题组介绍

PKU-EMBL 课题组聚焦高盐、高碱、强酸、低温等极端环境微生物资源挖掘，致力于发现未知微生物类群、新型酶、功能基因及生物活性物质合成基因簇，并构建从环境微生物“暗物质”到可计算知识与实验验证的高通量转化管道。

课题组自主开发分子生物学技术与智能分析方法，系统开展微生物基因组解析、功能注释、代谢途径发现、群落调控预测以及实验验证和应用转化。目前，课题组已对中国西北盐湖、酸湖、碱湖等极端水生态系统开展大规模宏基因组、宏转录组测序及环境理化因子测定，积累的数据量占全球同类数据的 80% 以上，为微生物组 AI 模型的训练、验证与工程化应用提供了独有的数据基础。团队正在建立“AI 预测—实验验证—模型迭代”的闭环研究体系，让计算发现真正进入实验验证与工程应用。

计算与 AI（干实验）方向

研究方向

  1. 功能基因组学与生物大模型：结合蛋白质语言模型、基因组语言模型及深度学习方法，识别常规分析流程容易遗漏的低丰度功能类群，挖掘新型酶、远缘功能基因、生物合成基因簇及环境适应元件。
  2. 代谢知识图谱与科学智能体：构建贯通“基因—酶—底物—产物—反应”的知识图谱，融合向量数据库、多组学证据检索及多智能体协同推理，实现新型代谢途径的自动发现、可行性评估与实验验证。
  3. 微生物群落世界模型：融合宏基因组、宏转录组、代谢物交换与环境因子，构建微生物群落的可计算模型，预测群落互作、状态演化及干预响应。
  4. 高性能软件与社群 Hub：开发生物信息学算法、数据库、自动化分析流程及可视化平台，探索 GPU 加速和智能体驱动的新型科研范式，建设开放共享的微生物组学社群 Hub。

基本要求

  1. 对 AI for Life Science、微生物组学或计算生物学有热情；课题组氛围开放，鼓励自主探索感兴趣的方向。
  2. 具备扎实的数学、统计学或计算机基础，掌握 Python、Rust、C/C++ 等至少一种编程语言。
  3. 精通 Vibe Coding，熟练使用 Skills 与 AI 编程工具，能够开展高效、可复现的研究与开发。
  4. 在生物大模型、科学智能体、生物信息学、高性能计算、知识图谱等方向至少有一项实践经验。
  5. 具有科研项目、开源项目、算法竞赛或完整软件开发经历者优先，但不作硬性要求。

培养方向与条件

聚焦 AI for Life Science，重点开展面向微生物资源挖掘与智能分析的科学智能体、生物大模型、代谢知识图谱与群落世界模型、高性能生物信息软件开发、生物合成基因簇挖掘及开源社群 Hub 建设等前沿研究，推动极端环境微生物从数据解析、功能预测到实验验证与应用转化。

课题组配备国内外导师团队、H200/A100/L40S 高性能 GPU 集群、丰富的极端环境多组学数据及产业转化机会。充分支持高质量实习，时间安排开放灵活，助力学生成长为具有国际视野和独立科研能力的交叉型人才。

实验微生物学（湿实验）方向

研究方向

  1. 极端微生物培养与鉴定：开发面向难培养、低丰度微生物的高效培养技术，开展菌株分离、鉴定与保藏，建设特色极端微生物资源库。
  2. 新型酶与功能基因验证：对 AI 模型预测的新型酶、远缘功能基因及环境适应元件开展克隆表达、功能鉴定和酶学性质研究。
  3. 生物合成基因簇异源表达：开展目标基因簇的克隆、组装、异源表达及产物检测，解析新型天然产物与生物活性物质的合成机制。
  4. 代谢途径解析与合成生物学：结合基因敲除、过表达、CRISPR 及代谢组学技术，验证新型代谢途径，优化表达系统与底盘细胞。
  5. 应用转化：推动功能菌株、极端酶及天然产物在污染物生物修复、生物质能源转化和医药开发等方向的应用。

基本要求

  1. 对极端环境微生物研究有热情；课题组氛围开放，鼓励自主探索感兴趣的方向。
  2. 具备扎实的分子生物学实验基础，熟练掌握常规分子克隆、PCR、蛋白表达纯化等技术。
  3. 有异源表达经验者优先，大肠杆菌、酵母或其他表达系统均可。
  4. 具有 CRISPR、代谢工程、天然产物分析或代谢组学经验者加分。
  5. 愿意学习 AI 辅助科研工具，与计算方向同学开展交叉合作。
  6. 动手能力强，能够独立设计和推进实验，具备规范的实验记录习惯。

培养方向与条件

聚焦极端环境微生物资源的实验室挖掘、功能验证与应用转化，重点开展新型及难培养微生物培养技术开发、生物合成基因簇异源表达与功能鉴定、极端酶挖掘与工程改造、新型代谢途径解析等研究，推动 AI 预测成果走向实验验证与实际应用。

课题组配备国内外导师团队、完善的实验平台、丰富的极端环境样品及多组学数据资源，并提供学术交流、产业转化与高质量实习机会，助力学生成长为具有国际视野的交叉型科研人才。

培养计划

  • 学制与类型：3 年学术型硕士（07 理学代码）
  • 培养模式：干湿结合的交叉培养，计算预测与实验验证紧密协作
  • 实习支持：鼓励并充分支持高质量实习，安排开放灵活
  • 培养目标：形成国际视野、独立科研能力和跨学科协作能力，推动研究成果从数据解析和功能预测走向实验验证与应用转化

申请方式

有意向的同学请将个人简历发送至 hwu202425@gmail.com，并抄送课题组老师邮箱 yuke.sz@pku.edu.cn，RP 研究计划可选。

邮件主题与附件请统一命名为：

命名格式：[保研申请] 姓名-学校-专业-喜欢的研究方向

欢迎私信介绍个人情况并进行交流，我们会逐一回复每位申请者。