Openings

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.

Research Directions

Graduate students, postdocs, and long-term visitors work within the following two complementary tracks.

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: enthusiasm for AI for Life Science, microbiome research, or computational biology; a solid foundation in mathematics, statistics, or computer science; proficiency in at least one language such as Python, Rust, C, or C++. Experience with biological foundation models, scientific agents, bioinformatics, high-performance computing, or knowledge graphs is a plus. AI-assisted development workflows are strongly encouraged.

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: solid molecular biology fundamentals and hands-on 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 rigorous experimental record-keeping and close collaboration with computational researchers.

Open Positions

Ph.D. Students

We recruit Ph.D. students through Peking University’s application-based admission and general admission channels (School of Environment and Energy, environmental engineering and related disciplines).

• Background: environmental science and engineering, microbiology, bioinformatics, computer science, mathematics, or automation; prior experience with metagenomics, machine learning, or molecular biology is a plus.
• We provide: a personal mentoring plan with domestic and international co-mentors; H200/A100/L40S GPU clusters and unique extreme-environment datasets; support for international exchange and academic conferences.

Recommended-Admission Master’s Students

We recruit master’s students through the recommended-admission (推免) channel: a three-year academic master’s program (discipline code 07, Science) with integrated computational–experimental training.

• Training model: integrated dry–wet projects, a domestic and international mentoring team, and flexible high-quality internships.
• Goal: develop interdisciplinary researchers who can move discoveries from data and models to experimental validation and application.

Postdoctoral Researchers

We invite applications from researchers who have earned (or will soon earn) a Ph.D. in environmental engineering, environmental science, microbiology, bioinformatics, computer science, or related fields.

• Profile: a solid publication record or demonstrated software/engineering ability aligned with the lab’s research directions; strong motivation for independent research.
• We provide: a competitive salary and benefits; one-on-one mentoring with a clear development plan; active support for applying to the National Postdoctoral Innovation Talent Program, NSFC Young Scientists Fund, postdoctoral funds, and international collaboration.

Joint-Training Students

Master’s and Ph.D. students enrolled at other universities or institutes may join the lab for joint training (联合培养), typically lasting 6–24 months.

• Model: co-supervision between the home advisor and the lab; research topics are designed to connect the home institution’s direction with the lab’s datasets, pipelines, and validation platforms.
• We provide: the same research resources and mentoring as lab members, plus support for co-authored publications and conference presentations.

Visiting Scholars & Students

We host visiting scholars (faculty and researchers), visiting Ph.D. students, and visiting undergraduates for flexible periods, typically 3–12 months.

• Model: visitors participate in ongoing projects or initiate small collaborative projects aligned with the lab’s directions; remote collaboration can be arranged for computational topics before or after the on-site period.

Research Interns

Undergraduate and graduate students seeking research experience are welcome as research interns, typically for 2–3 months or longer.

• Model: interns join a defined project with step-by-step mentoring; computational interns may work remotely after onboarding.
• Development: excellent interns receive strong recommendation letters and dedicated guidance for graduate-school applications.

How to Apply

Please email your CV (with transcripts and representative publications or projects where applicable) 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, replacing the category tag accordingly:

Format:[Ph.D. / Master's / Postdoc / Joint Training / Visiting / Intern] Name-University-Major-Preferred Direction

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