Job description:

1. Genomic data mining of clinical bulk sequencing

This group led by Dr. Robert J. Autry focuses on developing computational methods and workflows to answer clinically relevant questions across the range of childhood cancers by interrogating genomic and epi-genomic alterations. We aim to elucidate the mechanisms underlying cancer predisposition, tumorigenesis and therapeutic response to discover novel therapeutic targets and to improve diagnosis, prevention and clinical outcome in children.

Two postdocs will perform analyses on large-scale bulk genomic sequencing data sets (Whole genome/exome, RNA, methylation, other genomic and epigenomic data types). One position is working on cancer predisposition projects (rare variant burden testing, somatic second hit analysis and collaborations with other institutes). The other postdoc will work on high-grade glioma data to identify potential targets and vulnerabilities in this very high-risk cancer type. Both postdocs will independently manage their own academic research projects, coordinate to meet project deadlines under the mentorship and support of their supervisor. They will also prepare publications, present data both internally and externally, participate in relevant collaborations, and advise and inform other group members on relevant methodology and analytical approaches.

2. Single-cell / spatial genomics data mining

This group led by Dr. Supat Thongjuea aims to understand cellular heterogeneity and molecular mechanisms in childhood cancer. The group focuses on developing and applying computational approaches to analyze single-cell multi-omics and spatial information to uncover patterns and extract relevant knowledge underlying disease mechanisms.

One postdoc will work on data mining and integration of single-cell muti-omics/spatial datasets (RNA-seq, Spatial, ATAC-seq, copy-number and mutational analysis, all at single-cell resolution) including data from longitudinal samples from the same patients generated within the Division of Pediatric Neurooncology. The postdoc will apply existing or develop new computational, statistical, and machine learning techniques to uncover molecular patterns for understanding disease mechanisms (e.g., therapy resistance in cancer). The postdoc will independently manage own academic research, coordinate work to meet project deadlines while receiving mentorship and support from their supervisor. He/she will also prepare research publications and internal/external presentations, participate in relevant collaborations, and act as a source of information and advice to other group members on methodology and analytical approaches.

Requirements:

• The applicants must hold a master's degree and a PhD (or equivalent) in bioinformatics, computational biology, genomics and biostatistics, computer science or a related subject.
• The applicants have strong bioinformatics analytical skills and experience with Linux / Unix command lines and programming languages (e.g., C++, Python, R, and Bash).
• Experience in working in machine learning and high-performance computing environments is beneficial.
• Expertise in the analysis and integration of next-generation sequencing and other multi-omics data is a plus, ideally including single-cell or single-nucleus analysis and have a strong interest in oncology.
• Excellent communication skills and the ability to write publications, present research proposals and results, and represent the lab at meetings and conferences.
• Excellent interpersonal skills and the ability to work independently as well as part of a team.