University of Arizona
St. Louis
, Missouri
 United States
Postdoctoral
PhD
An NIH R01-supported postdoctoral fellow position in bioinformatics is available within the Kruer laboratory of the Phoenix Children’s Hospital Research Institute and University of Arizona College of Medicine – Phoenix. Work in the lab focuses on neurodevelopmental and neurodegenerative disorders of childhood, spanning both common and rare diseases. Successful candidates will have strong programming and analytical skills. The fellow will take ownership of projects integrating genomic (WES and WGS) and transcriptomic datasets across eukaryotic systems working collaboratively with bench scientists (molecular & cellular biologists, geneticists, neuroscientists) and physicians within the lab to characterize fundamental disease mechanisms. Opportunities in phenomics, metabolomics, algorithm development, and single cell analysis exist. Early career scientists will receive mentoring in proposal development, grantwriting, peer review and scientific communication to complement their professional development and prepare for positions in either academia or industry. The successful applicant will have opportunities to work both independently and as part of large collaborative teams across major research networks and to publish high-impact manuscripts.
- Utilize GPU-based NGS analysis to call and analyze SNVs, Indels, CNVs, SV, noncoding variants and triplet repeats
- Conduct statistical and/or wet lab-based validations, including enrichment and meta-analyses
- Conduct pathway/network (i.e. WGCN) analyses
- Contribute to phenomic data analysis and visualization
- Develop tools and algorithms
- Supervise undergraduate and graduate students
- Doctorate in bioinformatics, computer science, biology, or related field
- Highly-motivated, self-directed, dedicated candidates with strong recommendations
How to ApplyTo apply for this position, please send your CV
Analysis
Big Data
Biomedical
Biostatistics
Clinical
Development
Genome-wide
Genomics
Health
Interdisciplinary
Machine Learning
Models
Multi-disciplinary
NGS
Pathway
Phenotyping
Programming
Quantitative
Research
RNA
Single-cell
Software
Statistical
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