█ Recruitment Notice for Master's and Doctoral Students

Do you want to apply artificial intelligence methods to real problems in life sciences and medicine?

Do you hope that your algorithms, models, and data analysis skills will go beyond public datasets and classroom assignments, and instead be applied to real omics data, mass spectrometry data, and disease sample analysis?

Welcome to the BioNet Team at Xiamen University.

█ Team Profile

The BioNet Team at Xiamen University is based in the Department of Electronic Science, School of Electronic Science and Engineering, and the National Institute for Health and Medical Big Data, Xiamen University. The team has long been engaged in interdisciplinary research at the interface of artificial intelligence and life sciences, focusing on the development of new methods for key problems in biomedical signal processing, omics data analysis, and health and medical big data.

The team focuses on data modeling and methodological innovation for real biomedical problems. We emphasize the integration of algorithm development with real sample analysis, and aim to promote disease mechanism research and precision medicine through reliable, interpretable, and verifiable data analysis methods.

█ What Do We Study?

The research directions of the team can be summarized by three key themes: “AI + omics data analysis,” “AI + computational mass spectrometry and mass spectrometry imaging,” and “AI + precision medicine for major diseases.”

Specific research topics include machine learning, spatial multi-omics, computational mass spectrometry, mass spectrometry imaging, molecular network modeling, multimodal biomedical data integration, and health and medical big data analysis. Focusing on major diseases such as Alzheimer’s disease, hepatocellular carcinoma, diabetic kidney disease, nasopharyngeal carcinoma, and gastric cancer, the team develops methods for metabolic network analysis, spatial metabolomics analysis, mass spectrometry imaging data processing, and machine learning modeling. These methods are used to characterize metabolic abnormalities, spatial heterogeneity, and molecular interactions during disease development and progression.

█ Why Is This Team Suitable for Students with Interdisciplinary Backgrounds?

If you come from computer science, mathematics, electronic information, statistics, physics, or related fields, there are many real and complex biomedical data problems here that require your algorithmic thinking, modeling ability, and programming skills.

If you come from biology, medicine, chemistry, bioinformatics, or related fields, the team also provides an interdisciplinary platform for entering AI and data science research.

We welcome students from diverse academic backgrounds and value the process through which students grow from single-discipline training into interdisciplinary researchers. Students applying from other fields do not need to worry too much about differences in background. We care more about your foundational ability, willingness to learn, and genuine interest in interdisciplinary research.

█  What Kind of Research Training Will You Receive at BioNet?

At BioNet, you will participate in the full research process, from problem formulation and method design to data analysis, result validation, and paper writing.

We hope that students will not only learn how to use tools, but also gradually understand where research questions come from, why methods are designed in a certain way, whether data results are reliable, and how a paper can develop a clear scientific narrative.

The team encourages students to propose their own ideas under the guidance of their supervisor, and to gradually develop independent thinking, rigorous analysis, and clear communication skills. Research here requires solid effort, while also respecting students’ initiative and individual development.

█ Student Recruitment and Application Requirements

The team recruits both doctoral students and master’s students.

Recruiting units include the Department of Electronic Science, School of Electronic Science and Engineering, Xiamen University, and the National Institute for Health and Medical Big Data, Xiamen University.

Students from biology, medicine, computer science, mathematics, physics, electronic information, chemistry, statistics, bioinformatics, and related fields are welcome to apply.

Applicants are expected to have good learning ability, a strong interest in research, and the willingness to make sustained efforts. They should be able to read English literature smoothly and have basic programming experience in Python, R, MATLAB, or C++. Applicants with experience in machine learning, deep learning, graph models, mass spectrometry data analysis, bioinformatics, or omics data analysis will be given priority.

█ Join Us

If you hope to conduct research at the intersection of artificial intelligence and life sciences, and if you want your code, models, and data analysis skills to contribute to real medical problems, you are welcome to join the BioNet Team at Xiamen University.

Team website: https://bionet.xmu.edu.cn

Application: Please send your CV to jydong@xmu.edu.cn

We look forward to working with students who are interested in artificial intelligence, omics data analysis, mass spectrometry data processing, and biomedical big data research. Together at Xiamen University, we hope to explore meaningful scientific questions and conduct solid, valuable research.