Overview and Establishment

BGI-Research, formerly known as Shenzhen Huada Gene Research Institute, is a leading life science research institution. It is one of the first ten basic research institutions approved by the Shenzhen Municipal Government, having been established in 2007 and formally renamed to its current name in 2017. As a core research institute of the BGI Group, its predecessor can be traced back to the Beijing Huada Gene Research Center founded in 1999, which played a pivotal role in the International Human Genome Project ("1% Project").

Mission and Vision

Driven by a deep devotion to life science research, BGI-Research aims to provide strong scientific support in gene technology for the benefit of all humanity. The institute follows a triadic development model of "scientific discovery, technological invention, and industrial development," focusing on cutting-edge foundational questions in life sciences.

Research Focus and Core Technologies

BGI-Research is committed to frontier research in the reading, writing, and storing of life's code, organizing and implementing international scientific projects on life big data and disease prevention. The institute has cultivated a comprehensive research system covering areas such as bioinformatics, human genomics, agricultural genomics, microbiology, and marine biology.

BGI-Research concentrates on six key domains: the development of multi-omics technologies and equipment, mining of biological big data and intelligent analysis methods, multi-omics research on diseases and personal genomics, multi-omics and new technology research on agricultural species, microbial genome editing and modification applications, as well as comparative genomics and evolutionary studies of plants and animals.

The institute is also renowned for its world-leading core technologies. In 2021, BGI-Research developed Stereo-seq technology, which has reached subcellular-level resolution and is recognized as the highest-resolution spatial transcriptomic detection technology in the world, marking BGI-Research as a global leader in innovation.

Global Collaboration and Scientific Impact

Since its inception, BGI-Research has established close collaborations with over 500 scientific research organizations worldwide, operating more than 10 branch institutes. The institute has over 1,000 scientists dedicated to advancing life sciences research. BGI-Research has established significant international influence across various fields, including metagenomics, evolutionary biology, seed genomics and phylogenetics, developmental spatiotemporal omics, and DNA storage.

Research Achievements and Recognition

BGI-Research has achieved remarkable research output, with landmark studies being published in top-tier international journals. According to official BGI Group data, by the end of 2025, BGI had published a total of 5,928 papers, including 880 articles in leading academic journals such as Cell, Nature, and Science. In 2025 alone, BGI published 487 papers, including 111 ‘CNNS’ articles, with 21 in flagship journals, and has ranked first in the Nature Index for life sciences industrial organizations in the Asia-Pacific region for 10 consecutive years. Additionally, BGI researchers have been recognized as Clarivate’s Highly Cited Researchers for 11 consecutive years.

Job Details:

Responsibilities:

1. Based on performance requirements for industrial enzymes such as activity, stability, and solubility, utilize deep learning techniques and computational tools including protein language models (e.g., ESM, ProGen) and AlphaFold to perform rational design and multi-objective optimization of enzymes/proteins. For specific disease targets, combine generative models and structure-based constraints to achieve de novo design of nanobodies, cyclic peptides, and novel protein binders. Mine metagenomic and extreme environment sample data to discover novel functional elements.

2. Build and optimize a "dry-wet experiment closed-loop" algorithmic framework for protein design to shorten the design–experimentation iteration cycle. Track cutting-edge progress in generative AI and geometric deep learning, adapt these models to protein engineering scenarios, and complete model fine‑tuning, deployment, and performance optimization.

3. Collaborate closely with wet‑lab teams responsible for protein expression, purification, activity screening, etc., to facilitate experimental validation of designed candidates. Participate in external technical cooperation projects, including requirement analysis, solution delivery, and result presentation. Lead or contribute to research grant applications, patent drafting, and publication of high‑impact academic papers.

Qualifications:

1. Ph.D. in computational biology, bioinformatics, structural biology, chemistry, artificial intelligence, or other related interdisciplinary fields. Candidates whose doctoral research focused on AI‑driven protein design or protein engineering may have their research experience counted as equivalent work experience.

2. Proficiency in the PyTorch deep learning framework, with hands‑on experience in fine‑tuning protein language models, predicting mutation effects, and protein stability prediction.

3. Ability to handle small‑sample and high‑noise data, and independently design multi‑task, multi‑modal protein property prediction and fusion models.

4. Demonstrated experience in active learning and the construction of a dry‑wet experiment closed loop, with familiarity with model uncertainty estimation and full‑cycle iterative optimization methods.

5. Good understanding of the relationship between protein sequence, structure, and function. Proficiency in using AlphaFold, OpenFold, or similar tools for protein structure analysis, and capability to translate biological constraints into algorithmic features.

6. Mastery of computational methods such as equivariant graph networks and geometric diffusion models; proficiency with design tools including RFdiffusion and BoltzGen; ability to perform de novo protein, antibody, and binder design, as well as antibody affinity maturation optimization.

If you are interested, please send your resume to Christina: jobfair@chinajob.com

Apply Now