This is a current listing of job announcements related to Statistics. To submit a job for posting please use the Statistics Job Submission Form. Please note that jobs are posted within 24hrs of submission. If you have any questions, comments, or need to make a correction regarding your job announcement, please contact jobs@stat.ufl.edu.
Bioinformatics Scientist
Company Information
The NCI is an exciting place to work, and the position provides excellent learning opportunities and possibilities for professional advancement.
The Bioinformatics Scientist will work closely with Earl Stadtman Principal Investigators Dr. Diptavo Dutta and Dr. Mitchell Machiela, and also in collaboration with senior bioinformaticians, epidemiologists, and trainees within ITEB and across other branches in DCEG. They will be responsible for accessing and analyzing large genetic and genomic databases from current and past studies. This includes but is not limited to data manipulation, algorithmic implementation, statistical programming, genetic/genomic analyses and presenting results to scientific community within and beyond DCEG.
The position will provide support and expertise in installing, troubleshooting, and running analytical pipelines using open-source scientific software on Unix/Linux and Cloud-based platforms, and using publicly available bioinformatics and genomic databases and analysis pipelines. Data types can include genome-wide genotyping arrays, DNA sequencing, gene expressions, proteomic profiling, and methylation profiling, across a range of tissues and cancer types.
Qualifications: Required qualifications:
1. Master’s degree in bioinformatics, computational biology, data science, or a related field
2. Extensive programming experience in at least two of R, Python and C++.
3. Familiarity with Unix/Linux system, bash or similar scripts and high performance computing clusters
4. Team oriented with excellent written and verbal communication skills, with demonstrated ability to learn and adopt bioinformatics techniques and resources.
Preferred qualifications:
1. Experience in processing and analyzing large genetic and genomic files (VCFs, PLINK binary)
2. Experience in standard genetic association analysis software like PLINK, GCTA etc.
3. Familiarity and working knowledge of tools to query and investigate cancer genomics with publicly available data sources (such as dbGaP, TCGA, ENCODE, 1000 Genomes, GTEX, gnomAD, cBioPortal, TCPA)
4. Experience in performing statistical analysis of large datasets by running open source and custom developed analysis pipelines in R/Python/C++.