To apply, please submit the following to email@example.com.
The Peltz Laboratory at the Stanford University School of Medicine has multiple biomedical discovery programs, which generate complex datasets using advanced genetic and genomic methodology, that include: (i) Computational genetic analysis of multiple mouse models examining responses to drugs of abuse (cocaine, opiates and nicotine) that are generated by a multi-center (UO1) project; (ii) Developing artificial intelligence methods for genetic discovery; (iii) Analysis of human liver organoids to identify novel pathways affecting liver development, and for liver cancer treatment; (iv) A multi-center clinical trail that tests a new therapy, which emerged from a computational mouse genetic discovery, for preventing withdrawal in babies born to mothers that consume opiates; (v) Pharmacokinetic and metabolomic data obtained from mice with humanized livers, and from human subjects, using a new microcapillary sampling method. For each project, whole genome sequence, single-cell RNA-sequence, and/or metabolomic datasets are analyzed in an integrative fashion.
- Perform statistical and research for the above studies. This includes participating in study design; and performing data modeling, data analysis, and data management.
- Developing new (or improved) methods for computational genetic research and AI-based discovery
- Projects analyzed include big data/AI research for genetic mapping (using the mouse as the model organism); characterizing developmental systems using scRNA-seq and metabolomic data sets
- Provide statistical analysis for evaluating traditional biologic experiments.
- Ph.D. in Biostatistics, Statistics, or a related field
- Capable of functioning independently, but a demonstrated ability to work collaboratively
- Experience with multiple statistical programming languages such as R, SAS and Python
- A demonstrated ability to communicate with a range of different audiences Skilled in data modeling and the use of graphic interfaces
- Demonstrated expertise (publications) with statistical and bioinformatic methodology
- Experience with analysis of large scale genetic, metabolomic or scRNA-seq data
- Proven history of developing novel statistical or bioinformatic methodology
- A CV that emphasizes your bioinformatic/biostatistical experience and accomplishments
- List two individuals that will serve as references