Career Center

Dr. Longhua Guo, a postdoc with Dr. Leonid Kruglyak at the Department of Human Genetics at UCLA, will start his independent research lab at the University of Michigan in July 2022. He is actively recruiting a postdoc or a research staff with extensive quantitative skills in data analysis. Those with experiences in single cell genomics, epigenetics, statistics, or computer science are strongly encouraged to apply. The salary should be sufficient to support a high-quality life in Ann Arbor.

 

Dr. Guo's lab focuses on the cutting edge questions on aging and regeneration in two emerging model systems, the freshwater planarian and the leopard gecko. His lab will generate large datasets of genomics data, including spatial transcriptome data, single cell RNAseq data and DNA sequencing and genotyping data. Please check out more on his lab's website: https://guolabagingreg.com/.

 

For candidates who have been focused in data analysis, he/she is expected to interact with collaborators and members of the facilities to learn the analysis of specific datasets and to generate biological insights. The candidate will actively communicate with Dr. Guo to push the progress of projects and assist other members of the lab in biological research. These projects will involve data analysis of established technologies (e.g., RNAseq), emerging technologies (e.g., spatial transcriptome), and likely development of new algorithms in data analysis and visualization.

 

For candidates who have more balanced experiences in data analysis and experimentation on the bench, experiences in genetic manipulation (e.g., CRISPR/Cas9, tool use/development in traditional genetic systems such as flies, nematodes, fish and mice) will be preferred.

 

This is a great training opportunity in the fields of genetics, genomics, aging, stem cell and regenerative biology. We will promote both the training experiences and career development of the candidate.

Degree with statistics, bioinformatics, quantitative data analysis, data mining, computer science, genetics or related disciplines are preferred. Evidence of lab experiences with genomic data analysis is required. Proficiency in R or Python, Unix system, and coding is preferred.