Postdoctoral Associate- Computational Spatial Biology

We are hiring a Postdoctoral Associate in the Lester and Sue Smith Breast Center at Baylor College of Medicine. The Breast Center is a collaborative environment dedicated to breast health and disease research, as well as prevention, diagnosis, and treatment of different forms of breast cancer. We are looking for a candidate with a background in computational biology/bioinformatics who thrives in collaborative research.  For more information, visit https://www.bcm.edu/people-search/qian-zhu-119781

Job Duties

• Works in a Cancer Prevention & Research Institute of Texas (CPRIT) funded laboratory focused on computational cancer biology.
• Integrates various modalities of single-cell sequencing data and spatial genomics data to better understand the contribution of spatial multi-cellular environments to cancer formation, progression, and treatment.
• Contributes to the development of new methods or in the advancement of biological knowledge.
• Focuses on one of following:
  • Methodological development: The candidate will develop innovative methods to integrate multiple modalities of single cell data to disentangle cell-to-cell variability, and identify novel cell subpopulations that contribute to treatment resistance and cancer recurrence. Methods range from statistical, computational, to machine-learning based. These new methods are expected to benefit the community at large by being broadly applicable to different datasets.
  • Applied computational biology: The candidate will use a range of bioinformatics tools to analyze research data in order to glean interesting mechanistic insights during development and cancer progression, and treatment resistance. The candidate is expected to have excellent collaborative skills as he/she is expected to work alongside biologists and clinicians from other laboratories to make impactful discoveries.

Minimum Qualifications

• MD or Ph.D. in Basic Science, Health Science, or a related field.
• No experience required.

Preferred Qualifications

• Needs to have published a first-authored paper in a peer-reviewed journal.
• For candidate in the applied computational biology:
  • Knows how to use InferCNV to disentangle copy number variations in cancer genomics data.
  • Knows how to use Seurat, Monocle, SCENIC, Dynamo, Velocyto to infer both discrete cell populations and continuous developmental trajectories from single cell RNAseq data.
  • Knows how to infer cell-cell communication pairs.
  • Knowledge about how to use UK Biobank and All of Us Research data is a plus.
  • Knowledge about genetic analysis, such as variant calling, is a plus.
• For candidates in the methods development area:
  • Knowledge of probabilities and statistics, probabilistic modeling, Bayesian network, and deep programming knowledge of Python, R, is required.
  • Deep learning related experience is a plus.
  • The candidate needs to demonstrate ability to implement new algorithms in Python, R, and modify existing implementations written in one of the these languages.