Description:
Multiple postdoctoral research associate positions are immediately available in Dr. Haoyu Cheng’s Lab (https://hcheng-lab.github.io/) at the Department of Biomedical Informatics and Data Science (BIDS) at Yale University, School of Medicine. Successful candidates will engage in the development of innovative computational algorithms for genome analysis. Potential projects include genome assembly, read alignment, variant calling, pangenome analysis, as well as other related research on cancer and plant genomes.
Preferred qualifications:
- Ph.D. in Computer Science, Bioinformatics, Computational Biology, Statistics, Genetics, or a related field.
- Strong programming skills. Demonstrated experience in developing algorithms using high-performance languages such as C, C++, or Rust is desirable.
- Demonstrated expertise in analyzing large-scale genomic sequence data.
- A strong track record of scientific publications.
- Good oral and written communication skills in English.
How to apply:
Please email your CV to Dr. Cheng at [email protected] with the subject ‘Postdoc Application - Cheng Lab’. Applications will be reviewed on a rolling basis.
About the PI:
Dr. Haoyu Cheng is an Assistant Professor at the Department of Biomedical Informatics and Data Science (BIDS) at Yale University, starting in July 2024. His research primarily focuses on developing computational methods for genomic data, with a particular emphasis on de novo genome assembly and its applications. He has developed a suite of algorithms for de novo genome assembly, including hifiasm (Cheng et al, Nature Methods, 2021), hifiasm (Hi-C) (Cheng et al, Nature Biotechnology, 2022) and hifiasm (UL) (Cheng et al, Nature Methods, 2024). These algorithms have been widely used by numerous large-scale sequencing projects, such as the Human Pangenome Reference Consortium (HPRC), the Genome in a Bottle (GIAB), the Vertebrate Genomes Project
(VGP), and the Darwin Tree of Life project. His works received the recognition of the K99/R00 Pathway award from the National Institutes of Health (NIH). For more information, please see https://hcheng-lab.github.io/