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RESPONSIBILITIES

Job Summary/Basic Function:
Public data repositories for high-throughput data (e.g., microarray, RNA-seq, methylation, CHiP-seq, etc) are growing rapidly. Our lab is interested in developing meta-analytic approaches to identifying significant trends within these databases.

We're seeking a Post-Doctoral Associate to help us to compile, normalize, quality check and analyze high-throughput data from a variety of sources, including public repositories, to better understand several biological phenomenon such as transcriptional modularity, factors that influence changes in transcriptional states and/or regulation, conditions which increase transcriptional variability and determine alternative splicing. This work has the potential to contribute to the discovery and characterization of novel genes and ncRNAs, particularly those that can potentially affect human disease.

The candidate should be familiar with methods of quality checking and processing high-throughput data (e.g., FASTQC for RNA-seq data) and will work on developing methods for meta-analysis of these data repositories. The goal of the project is to identify factors that contribute to and/or correlate with changes from healthy to disease states, and to rapidly advance our understanding of non-coding RNA biology in general via functional prediction methods. We have many collaborative projects and anticipate many opportunities for co-authorship for the candidate. The candidate should have solid programming skills and demonstrable programming experience (e.g., publications where they contributed to algorithm development). Familiarity with software and methods for processing next-generation sequencing data is greatly preferred. We have access to a strong array of analytical computing resources.

REQUIREMENTS

Minimum Qualifications:

Ph.D. in a quantitative discipline such as Computer Science, Bioinformatics, or in Physics/Biology with a strong quantitative background.
Strong experience in writing computer programs is required, in at least one widely used programming language (e.g., Python, C++, R, Visual Basic).
Strong personal initiative and the ability to work effectively as part of a team.

Minimum Degree Required: Doctorate

Preferred Qualifications:

Experience analyzing/processing next-generation sequencing data preferred.
Machine learning and/or large scale data mining experience preferred.
Familiarity with statistics is a plus.

Physical Demands:
Regularly required to stand, use manual dexterity, talk or hear; Frequently required to walk and reach with hands and arms; occasionally sit; frequently lift and/or move up to 50 pounds; vision abilities include close vision, color vision, depth perception and ability to adjust focus.
Potential exposure to fumes, airborne particles, or rodent dander. May work with toxic, caustic chemicals, radioactive materials, liquid nitrogen, human tissue or blood. Potential risk of electrical shock. Occasional exposure to cold 4C, and/or extreme cold -80C from freezers. Noise level is usually moderate.

TERMS

Position Type: SCIENTIFIC
Classification: Ongoing
Status: Full-Time

HOW TO APPLY

To apply, visit http://apptrkr.com/549081

Posting Number: 0000757
Title: Post-doctoral Fellow
Department: ACI - WREN

Special Instructions to Applicants:

Please send CV to or Contact:
Jonathan D. Wren, Ph.D.
Oklahoma Medical Research Foundation
825 N.E. 13th Street, Rm MC103
Oklahoma City, Oklahoma 73104-5005
Jonathan-Wren[at]OMRF.org

Application Type Accepted: General Application
Required Applicant Documents: Resume/Curriculum Vitae
Optional Applicant Documents: Cover Letter

Discussion forums: Opportunity: Post-doctoral Fellow -- Bioinformatics and transcriptional network analysis (JWREN) @ OMRF -- Oklahoma City, OK (US)

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