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Old 10-17-2013, 05:28 PM   #1
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Default Computational Biologist Position GlaxoSmithKline

Computational Biologist Position Open at GlaxoSmithKline R&D in Philadelphia, PA

The Computational Biology Department supports Drug Discovery and Development through the integrative analysis of internal and external biomedical data, combining strong computational skills with biological insight. Our analysis competencies are quite diverse, spanning areas such as microbiome/host pathogen analysis, drug repositioning, disease indications, sequence analysis, comparative genomics, pathways and networks, molecular evolution, RNA/protein expression, text mining, structural biology, population/molecular genetics, diagnostics and business opportunities. We contribute to projects at all phases of the pipeline from initial target discovery through clinical phases and launched products. Our work results in real impacts on new medicines for highly important diseases of unmet medical need.

Job Details
We currently have one opening within the GSK Computational Biology Department for Computational Biologists.
As a Computational Biologist you will have a strong technical background in bioinformatics / computational biology / computer science allied with an excellent knowledge of biology. Your primary role will be to identify, invent, develop, implement and/or use computational biology methods to impact drug discovery and development. You will be comfortable with working in a dynamic, fast changing environment, and able to communicate complex informatics principles, methods, analyses and results to scientists from diverse backgrounds. You will be expected to maintain and develop appropriate computational biology skills, as well as, your internal and external scientific profile through presentations and peer-reviewed publications.

Basic qualifications required:
PhD in bioinformatics / computational science / biomedical engineering/ biological sciences or equivalent research experience.
Demonstrated knowledge of biological mechanisms and application of computational biology methods to impact drug discovery and development
Demonstrated skills to programmatically collect, combine, mine and analyze complex biological data.
Demonstrated skills in the analysis of multiple large scale genomic and genetic platforms such as transcriptomics, proteomics and DNA sequencing (including NextGen sequencing).
Strong communication skills and ability to work well in a matrix environment
Publication record in peer reviewed journals

Some recent publications by GSK Computational Biology:

1. Hurle MR, et al. 2013. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 2013 Apr;93(4):335-41.
2. Sanseau P, et al. 2012. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012 Apr 10;30(4):317-20
3. Smith SB, et al. 2012. Identification of common biological pathways and drug targets across multiple respiratory viruses based on human host gene expression analysis. PLoS One. 2012;7(3):e33174.
4. Collison M, et al. 2012. Data mining the human gut microbiota for therapeutic targets. Briefings in Bioinformatics. 2012 Nov;13(6):751-68
5. Kumar V, et al. 2011. Comparative genomics of Klebsiella pneumoniae strains with different antibiotic resistance profiles. Antimicrobial Agents and Chemotherapy. 55:4267-4276.
6. Yao, L. et al. (2011) Electronic health records: Implications for drug discovery. Drug Discovery Today 16: 594-599
7. Buchan, N.S. et al. (2011) The role of translational bioinformatics in drug discovery. Drug Discovery Today 16: 426-434
8. Brown, J.R. et al. (2011) Computational approaches for selecting host-pathogen drug targets. Drug Discovery Today 16: 229-236
9. Brown, J.R. and Auger, K.R. (2011) Phylogenomics of phosphoinositide lipid kinases: perspectives on the evolution of second messenger signalling and drug discovery: BMC Evolutionary Biology: 11, 4
10. Zhang, Y. et al. (2010) Molecular evolutionary analysis of cancer cell lines Mol. Cancer Therapeutics: 9, 27-291
11. Gamo F-J, et al. (2010). Thousands of chemical starting points for antimalarial lead identification. Nature. 465:305-310.
12. Li, Y. and P. Agarwal (2009). "A pathway-based view of human diseases and disease relationships." PLoS One 4(2): e4346.

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bioinfomatics, computaltional biology, drug discovery, microbiome, ngs

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