Job Description:
Opportunity for a post-doctoral candidate to conduct independent research, under the supervision of Harvard Medical School faculty, in a several ongoing large, cohort studies. Ph.D or MD required. Responsibilities: Postdoctoral fellowship is available to conduct research in Computational Biology. Opportunity for a post-doctoral candidate to conduct independent research, under the supervision of Harvard Medical School faculty, in a several ongoing large, cohort studies. Despite the successes of genome-wide association studies (GWAS), important challenges remain that limit their impact on human biology and medicine, especially the fact that in the majority of cases the causal loci are located in non-coding regions and poorly understood. Recent advances in human regulatory and functional genomics (ENCODE, Roadmap Epigenomics Project and GTEX) using 3D genomic structure (such as HI-C seq) and gene editing technologies (such as CRISPR/Cas9) allow us to overcome these challenges and gain a systematic understanding of the role of GWAS variants in human disease and complex traits. To translate human genetics findings from GWAS and NGS association studies to actionable therapeutic-target candidates, the research team has been developing machine learning approaches by incorporating information from human tissue-specific regulatory landscapes to predict causal/functional variants and their targeted genes under each GWAS locus. The Post-doc will carry out independent research and participate in projects that (1) perform genome-wide scan on the NHLBI TopMED WGS samples and UK BioBank samples to identify less common and rare variants associated with aging relevant phenotypes; (2) apply 3D genomic structure (such as chromatin confirmation capture-seq, ATAC-seq), functional genomics (such as chip-seq data), biological networks and systems biology approaches to identify causal variants (especially for those non-coding variants) and their targeted genes for complex disorders; (3) improve and/or apply machine learning approaches to better predict causal/functional variants/targeted genes (4) involve high-throughput wet lab experiments to functionally validate findings in cellular models by MPRA and/or CRIPSR/Cas9 gene editing approaches. The Postdoc will interact with a diverse scientists of statistical geneticists, computational biologists and bench scientists at Harvard Medical School, Boston University, and Broad Institute of MIT and Harvard. The fellow would be involved in developing the analytic tools, conducting the analysis, and preparing manuscripts. This an ideal opportunity for independent research using these state-of-the-art outcome measures under the mentorship of senior investigators. Strong interpersonal and communication skills are required as this person will interact with a large research team including senior scientists, data managers, and biostatisticians. An established publication record and excellent writing skills are preferable. Qualifications: •PhD, ScD and/or MD in statistical genetics, quantitative genomics, computational biology, or systems biology/genomics with experiences in human genetic research . •Deep expertise in genome-wide association studies; NGS association studies in variant phenotypes and traits, human epigenomics, regulatory genomics networks, network modeling algorithms and machine learning algorithms •Experience using high-throughput data such as next generation sequencing, RNA sequencing, Chip-seq, DNAse-seq and/or Hi-C seq as well as experience using the ENCODE and Roadmap Epigenomics Project data •Expertise in the statistical analyses of next generation sequencing data or bioinformatics (especially in network/pathway modeling) •Experience in analyzing high throughput web-lab experiments (such as chip-seq, RNA-seq, chromosome conformation capture, or mass parallel reporter assay) •Able to present findings and write papers in the field of study •Expertise with statistical genetics and network modeling algorithms •Familiarity and involvement in use of large scale omics data, such as ENCODE, Roadmap Epigenomics Project, GTEX, eQTL datasets, Hi-C and RNA-seq data etc •Involvement in GWAS and NGS (WES/WGS) association studies •Familiarity in use of human genetic resources (browsers and datasets) such as 1,000G Project, UK10K, ESP Project, EXACT, UK Biobank, etc •Experience with molecular genetic approaches such as WGS, WES, RNASeq, chip-seq, chromosome conformation capture •Experience/knowledge in molecular characterization and functional studies geared towards elucidating the mechanistic basis for genetic associations (such as mass parallel reporter assay, etc.) •Publication track record in peer-review journals •Comfort in working in a highly interactive and vibrant environment designed to promote frequent interaction and brainstorming •Passion and aptitude for teamwork •Excellent communication skills and the ability to influence and inspire
Additional Info:
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Opportunity for a post-doctoral candidate to conduct independent research, under the supervision of Harvard Medical School faculty, in a several ongoing large, cohort studies. Ph.D or MD required. Responsibilities: Postdoctoral fellowship is available to conduct research in Computational Biology. Opportunity for a post-doctoral candidate to conduct independent research, under the supervision of Harvard Medical School faculty, in a several ongoing large, cohort studies. Despite the successes of genome-wide association studies (GWAS), important challenges remain that limit their impact on human biology and medicine, especially the fact that in the majority of cases the causal loci are located in non-coding regions and poorly understood. Recent advances in human regulatory and functional genomics (ENCODE, Roadmap Epigenomics Project and GTEX) using 3D genomic structure (such as HI-C seq) and gene editing technologies (such as CRISPR/Cas9) allow us to overcome these challenges and gain a systematic understanding of the role of GWAS variants in human disease and complex traits. To translate human genetics findings from GWAS and NGS association studies to actionable therapeutic-target candidates, the research team has been developing machine learning approaches by incorporating information from human tissue-specific regulatory landscapes to predict causal/functional variants and their targeted genes under each GWAS locus. The Post-doc will carry out independent research and participate in projects that (1) perform genome-wide scan on the NHLBI TopMED WGS samples and UK BioBank samples to identify less common and rare variants associated with aging relevant phenotypes; (2) apply 3D genomic structure (such as chromatin confirmation capture-seq, ATAC-seq), functional genomics (such as chip-seq data), biological networks and systems biology approaches to identify causal variants (especially for those non-coding variants) and their targeted genes for complex disorders; (3) improve and/or apply machine learning approaches to better predict causal/functional variants/targeted genes (4) involve high-throughput wet lab experiments to functionally validate findings in cellular models by MPRA and/or CRIPSR/Cas9 gene editing approaches. The Postdoc will interact with a diverse scientists of statistical geneticists, computational biologists and bench scientists at Harvard Medical School, Boston University, and Broad Institute of MIT and Harvard. The fellow would be involved in developing the analytic tools, conducting the analysis, and preparing manuscripts. This an ideal opportunity for independent research using these state-of-the-art outcome measures under the mentorship of senior investigators. Strong interpersonal and communication skills are required as this person will interact with a large research team including senior scientists, data managers, and biostatisticians. An established publication record and excellent writing skills are preferable. Qualifications: •PhD, ScD and/or MD in statistical genetics, quantitative genomics, computational biology, or systems biology/genomics with experiences in human genetic research . •Deep expertise in genome-wide association studies; NGS association studies in variant phenotypes and traits, human epigenomics, regulatory genomics networks, network modeling algorithms and machine learning algorithms •Experience using high-throughput data such as next generation sequencing, RNA sequencing, Chip-seq, DNAse-seq and/or Hi-C seq as well as experience using the ENCODE and Roadmap Epigenomics Project data •Expertise in the statistical analyses of next generation sequencing data or bioinformatics (especially in network/pathway modeling) •Experience in analyzing high throughput web-lab experiments (such as chip-seq, RNA-seq, chromosome conformation capture, or mass parallel reporter assay) •Able to present findings and write papers in the field of study •Expertise with statistical genetics and network modeling algorithms •Familiarity and involvement in use of large scale omics data, such as ENCODE, Roadmap Epigenomics Project, GTEX, eQTL datasets, Hi-C and RNA-seq data etc •Involvement in GWAS and NGS (WES/WGS) association studies •Familiarity in use of human genetic resources (browsers and datasets) such as 1,000G Project, UK10K, ESP Project, EXACT, UK Biobank, etc •Experience with molecular genetic approaches such as WGS, WES, RNASeq, chip-seq, chromosome conformation capture •Experience/knowledge in molecular characterization and functional studies geared towards elucidating the mechanistic basis for genetic associations (such as mass parallel reporter assay, etc.) •Publication track record in peer-review journals •Comfort in working in a highly interactive and vibrant environment designed to promote frequent interaction and brainstorming •Passion and aptitude for teamwork •Excellent communication skills and the ability to influence and inspire
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[Click Here to Access the Original Job Post]