The research lab of Dr. James Costello in the Department of Pharmacology is seeking a postdoctoral researcher that is interested in developing computational methods to disentangle co-occurring conditions using multi-omics data in Down syndrome. The position will be jointly mentored by Dr. Casey Greene in the Department of Biomedical Informatics and is funded by the NIH, INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project. The ideal candidate will have a background in multi-omic data processing, analysis, and the development and application of deep learning methods, with a strong interest in understanding molecular mechanisms of disease. The position will be highly collaborative working with a diverse group of researchers. It will also be expected that this position will publish academic research articles and contribute to lab efforts in mentoring students, grant writing, and manuscript review.
Key Responsibilities:
- Participate in the design and initiation of research projects
- Independently carry out literature reviews, analysis, and interpretation of results with guidance from the PI
- Collaborate with the PI, other members of the lab, and local and international collaborators
- Maintain transparent and easy-to-follow documentation of research projects from start to finish, including reproducible and sharable code.
Minimum Qualifications: Applicants must meet minimum qualifications at the time of hire.
- PhD in genetics, bioinformatics, computational sciences, data science, machine learning, biostatistics, applied mathematics, computer science, or related fields.
Preferred Qualifications:
- At least 3 years of experience in R, Python, and/or a comparable programming language, preferably in a high-performance cluster computing environment and using version control.
- Experience developing and/or applying machine learning methods to a problem in some domain such as biology, social science, economics, etc. Alternatively, experience working with high-throughput omics, text, or graph-like datasets.
- Proficiency in scientific communication with evidence of first-author manuscript(s) in peer-reviewed journals and oral presentation(s) at national or international conferences.
- Attributable contributions to source code.
Knowledge, Skills and Abilities:
- Practical experience in the Python data wrangling and machine learning stack including pandas, matplotlib, scikit-learn, and/or pytorch and/or practical experience in using R for omics (esp. transcriptome) data analysis and integration.
- Evidence of working in collaborative teams and positively contributing to others’ projects.
- Demonstrated willingness to give/take constructive feedback and to sustain a supportive and inclusive lab environment.
Please apply through the following link: cu.taleo.net/careersection/2/jobdetail.ftl?job=35653&lang=en