Job ID:
Job date: 2018-03-19
End Date:
Company : National Institutes of Health Country : Role : Other
Job date: 2018-03-19
End Date:
Company : National Institutes of Health Country : Role : Other
Job Description:
We are seeking to recruit an individual with responsibilities in: 1) computational analysis, integration, and modeling of diverse data sets; 2) mentor, collaborate, and set up software and computational infrastructure and tools to enable other members of the lab with less computational experience or skills (e.g., post-baccalaureate fellows, postdoctoral fellows and staff) to perform computational data analyses and modeling; 3) liaison and work with NIH/NIAID IT and high-performance computing teams to set up, develop, and maintain necessary computational infrastructure and tools for research in the group; 4) help manage the computational side of the lab and work with the PI to develop and maintain an intellectual simulating, collaborative, and productive research environment and culture. This position involves close collaboration with and mentoring of trainees, including both experimentalists and computational biologists, to answer research questions of interest by developing and performing computational analyses and modeling. Data from high-throughput assays are routinely generated and analyzed in the lab, including those from microarrays, bulk and single-cell RNAseq, ATAC-seq, flow and CyTOF phenotyping, multiplex analysis of serum proteins, SNP genotyping, and microbiome profiling. We also conduct "bottom up" dynamical modeling, for example, starting from networks of molecular/cellular interactions. Qualifications: The ideal candidate will have a Ph.D. in computational biology, bioinformatics, systems biology, biophysics, or relevant disciplines and 3+ years of hands-on research experience with bioinformatics and the analysis and modeling of large-scale data sets. She/he should possess a sound knowledge of statistics and quantitative modeling, and solid computer programming skills with proficiency in R and at least one scripting language (Perl/Python/Ruby); solid experience with database design and construction, and dynamical system theory and modeling are pluses; proficiency in C/C++ and/or Java as well as experience with multiple OS platforms (Windows, MAC, Linux) are also pluses, as is research experience or formal training in the biomedical sciences.
Additional Info:
The Systems Genomics and Bioinformatics Unit at NIAID/NIH (PI: John Tsang, who also co-directs the NIH Center for Human Immunology) studies the immune system in health and disease by using and developing a combination of computational and experimental approaches. The major goal is to develop a quantitative, predictive understanding immune system states and responses to perturbations. For more information about our work, please see representative publications below. Representative work from the lab: Tsang, John S. "Utilizing Population Variation, Vaccination, and Systems Biology to Study Human Immunology." Trends in Immunology 36, no. 8 (January 8, 2015): 479-93. https://doi.org/10.1016/j.it.2015.06.005 . Martins, Andrew J.*, Manikandan Narayanan*, Thorsten Prüstel, Bethany Fixsen, Kyemyung Park, Rachel A. Gottschalk, Yong Lu, et al. "Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network." Cell Systems 4, no. 4 (April 26, 2017): 379-392.e12. https://doi.org/10.1016/j.cels.2017.03.002 . (co-first authors) Lu, Yong, Angelique Biancotto, Foo Cheung, Elaine Remmers, Naisha Shah, J. Philip McCoy, and John S. Tsang. "Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations." Immunity 45, no. 5 (November 15, 2016): 1162-75. https://doi.org/10.1016/j.immuni.2016.10.025 . Sparks, Rachel, William W. Lau, and John S. Tsang. "Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing." Immunity 45, no. 6 (December 20, 2016): 1191-1204. https://doi.org/10.1016/j.immuni.2016.12.008 . Tsang, John S.*, Pamela L. Schwartzberg*, Yuri Kotliarov, Angelique Biancotto, Zhi Xie, Ronald N. Germain, Ena Wang, et al. "Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses." Cell 157, no. 2 (April 10, 2014): 499-513. https://doi.org/10.1016/j.cell.2014.03.031 . (co-senior/corresponding authors) Shah, N.*, Guo, Y.*, Wendelsdorf, K.V.*, Lu, Y., Sparks, R., and Tsang, J.S. (2016). A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nature Biotechnology 34, 803. (* co-first authors) To Apply: Please contact Dr. John Tsang at john.tsang@nih.gov . NIH is dedicated to building a diverse community of trainees. [Click Here to Access the Original Job Post]
We are seeking to recruit an individual with responsibilities in: 1) computational analysis, integration, and modeling of diverse data sets; 2) mentor, collaborate, and set up software and computational infrastructure and tools to enable other members of the lab with less computational experience or skills (e.g., post-baccalaureate fellows, postdoctoral fellows and staff) to perform computational data analyses and modeling; 3) liaison and work with NIH/NIAID IT and high-performance computing teams to set up, develop, and maintain necessary computational infrastructure and tools for research in the group; 4) help manage the computational side of the lab and work with the PI to develop and maintain an intellectual simulating, collaborative, and productive research environment and culture. This position involves close collaboration with and mentoring of trainees, including both experimentalists and computational biologists, to answer research questions of interest by developing and performing computational analyses and modeling. Data from high-throughput assays are routinely generated and analyzed in the lab, including those from microarrays, bulk and single-cell RNAseq, ATAC-seq, flow and CyTOF phenotyping, multiplex analysis of serum proteins, SNP genotyping, and microbiome profiling. We also conduct "bottom up" dynamical modeling, for example, starting from networks of molecular/cellular interactions. Qualifications: The ideal candidate will have a Ph.D. in computational biology, bioinformatics, systems biology, biophysics, or relevant disciplines and 3+ years of hands-on research experience with bioinformatics and the analysis and modeling of large-scale data sets. She/he should possess a sound knowledge of statistics and quantitative modeling, and solid computer programming skills with proficiency in R and at least one scripting language (Perl/Python/Ruby); solid experience with database design and construction, and dynamical system theory and modeling are pluses; proficiency in C/C++ and/or Java as well as experience with multiple OS platforms (Windows, MAC, Linux) are also pluses, as is research experience or formal training in the biomedical sciences.
Requeriments :
Skills :
- Bioinformatics
- Database design
- Lunix/Unix
- Modeling
- Programing in C
- Programing in C++
- Programing in Python
- Programing Skills
- Programming in Java
- Programming in PERL
- Programming in R
- Statistics
Areas :
Additional Info:
The Systems Genomics and Bioinformatics Unit at NIAID/NIH (PI: John Tsang, who also co-directs the NIH Center for Human Immunology) studies the immune system in health and disease by using and developing a combination of computational and experimental approaches. The major goal is to develop a quantitative, predictive understanding immune system states and responses to perturbations. For more information about our work, please see representative publications below. Representative work from the lab: Tsang, John S. "Utilizing Population Variation, Vaccination, and Systems Biology to Study Human Immunology." Trends in Immunology 36, no. 8 (January 8, 2015): 479-93. https://doi.org/10.1016/j.it.2015.06.005 . Martins, Andrew J.*, Manikandan Narayanan*, Thorsten Prüstel, Bethany Fixsen, Kyemyung Park, Rachel A. Gottschalk, Yong Lu, et al. "Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network." Cell Systems 4, no. 4 (April 26, 2017): 379-392.e12. https://doi.org/10.1016/j.cels.2017.03.002 . (co-first authors) Lu, Yong, Angelique Biancotto, Foo Cheung, Elaine Remmers, Naisha Shah, J. Philip McCoy, and John S. Tsang. "Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations." Immunity 45, no. 5 (November 15, 2016): 1162-75. https://doi.org/10.1016/j.immuni.2016.10.025 . Sparks, Rachel, William W. Lau, and John S. Tsang. "Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing." Immunity 45, no. 6 (December 20, 2016): 1191-1204. https://doi.org/10.1016/j.immuni.2016.12.008 . Tsang, John S.*, Pamela L. Schwartzberg*, Yuri Kotliarov, Angelique Biancotto, Zhi Xie, Ronald N. Germain, Ena Wang, et al. "Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses." Cell 157, no. 2 (April 10, 2014): 499-513. https://doi.org/10.1016/j.cell.2014.03.031 . (co-senior/corresponding authors) Shah, N.*, Guo, Y.*, Wendelsdorf, K.V.*, Lu, Y., Sparks, R., and Tsang, J.S. (2016). A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nature Biotechnology 34, 803. (* co-first authors) To Apply: Please contact Dr. John Tsang at john.tsang@nih.gov . NIH is dedicated to building a diverse community of trainees. [Click Here to Access the Original Job Post]