We are actively recruiting multiple postdoctoral scholars, graduate students, visiting scholars, and Master’s/undergraduate students to join our interdisciplinary research team. We welcome highly motivated, independent, and creative scientists who are excited to build predictive & programmable biology together and to tackle critical challenges in cancer and aging. 

Research Focus: Our lab develops and applies biologically grounded multimodal AI technologies to decode genome regulation and pioneer next-generation cell engineering and de novo cell type design. 

Genome regulation and cell fate determination are intrinsically multimodal, integrating genomic DNA sequences, protein regulators, and their complex interactions. Over the past few years, we have built foundational multimodal genomics AI models – such as C.Origami and Chromnitron — that understand the core principles of genome regulation and enabled high-throughput in silico screening to accelerate biological discoveries. We continue to develop and apply innovative technologies to achieve a predictive, interpretable, and programable understanding of cell fate determination in health and diseases.

Candidate: We seek motivated and ambitious scientists across career stages with strong backgrounds & interests in one or more of the following areas:

1) Multimodal machine learning and generative AI

2) Single-cell multi-omics and computational systems biology 

3) Gene regulation and cell fate determination

4) Synthetic biology and bioengineering applied to aging, cancer biology, stem cell & regenerative biology, immunology, or molecular neuroscience 

Successful candidates will demonstrate strong independent research motivation, problem-solving ability, and enthusiasm for working in a multidisciplinary and collaborative environment. You will develop and/or apply state-of-the-art multimodal genomics AI technologies to address critical questions in aging, regeneration, and cancer biology.

Location: Broad Institute of MIT and Harvard & Massachusetts General Hospital/Harvard Medical School

Contact: Please email boxialab{at}gmail.com with your CV and a brief description of your background, research interest(s), and career goal(s). Selected candidates will be invited for an initial Zoom or in-person interview. 2–3 reference letters will be requested at a later stage.

Mentorship & Support Commitment: 

We are committed to supporting the career and personal development of all lab members. We believe that the best science comes from good teams fueled by passion, motivation, perseverance, and – probably much underestimated – a supportive environment. We build an inclusive and supportive lab environment where everyone can achieve their science dreams and career goals regardless of their background, gender, ethnicity, race, or nationality. 

Graduate Student Candidates: Our lab is ready to host students from diverse research backgrounds and support bold research projects from multimodal AI and single-cell multi-omics technologies to aging and cancer biology challenges. Current graduate students from any of the Harvard Integrated Life Sciences (HILS) Ph.D. programs at Harvard Medical School and Harvard University are encouraged to contact Bo and discuss customized rotation and thesis projects.  

Prospective students who are interested in the PhD programs must apply and be admitted through one of Harvard’s HILS PhD programs — for example, the PhD programs in Biological and Biomedical Sciences, Biomedical Informatics, and Systems, Synthetic, and Quantitative Biology​. Once admitted, students are welcome to contact Bo via email.

Master and Undergraduate Students: Highly motivated students are encouraged to reach out about research opportunities in our lab. Bo's career started with a eureka moment during his undergraduate research, which eventually led to multiple innovative epigenomic technologies. We strive to provide research and training opportunities for motivated master and undergraduate students. We welcome students who think beyond boundaries and are eager to grow into next-generation scientific leaders.