JOB: postdoc in computational / statistical genetics at Mount Sinai in New York

A postdoc position for a statistician with biology/genomics skills has become available in Rui Chang’s lab at Mount Sinai in New York City.
See below for details.

We are seeking a motivated and passionate postdoctoral researcher to work on computational systems biology and human diseases modeling. The successful candidate will work in an exciting interdisciplinary environment. We routinely interact with other Mount Sinai labs (clinical, wet-lab or computational groups) as well as other national and international collaborators, providing numerous opportunities for exciting research and high impact publications. We work on data across a range of conditions and currently we have collaborations on Alzheimer’s disease, pluripotent and differentiated stem cells and a variety of cancers.
We are an interdisciplinary lab with our research spanning from advanced mathematical methods development to applied, real-world disease modeling. Accordingly we are interested in candidates with profiles fitting into one of two categories:

1. Computation methods and software development:
The applicant is expected to focus on novel method developments and software development. Prospective candidates should have a recent PhD degree in computer science specialized in machine learning, mathematics or statistics. Strong working experience in Bayesian networks and other graphical models is highly preferred. The successful candidate must have strong, demonstrable programming skills in C/C++, Java, R or similar. Programming skills in additional languages is a plus. Basic knowledge in biology and hands-on experience in computational biology is highly desired but not required. The candidate will be responsible for developing cutting-edge machine learning approaches, and is expected to develop software platforms towards real-world human disease network modeling and drug target prediction. The position holder will also be expected to apply these methods to real-world data and help with data processing and analysis tasks.

2. Bioinformatics and real-world disease modeling:
The applicant is expected to focus on real-world disease modeling and have a strong background in bioinformatics. Prospective candidates should have a recent PhD degree in computer science, bioinformatics, biostatistics, computational biology or a related field. The successful candidate should have a solid working knowledge of (molecular) biology and genomics and demonstrable hands-on experience in analyzing, visualising and integrating omics data. The applicant should have good programming skills in R (preferred), Python or Matlab. Programming skills in additional languages is a plus. The candidate should have a solid comprehension of state-of-the-arts bioinformatics tools for processing, analyzing and annotating sequencing data as well computational algorithms relevant to omics data analysis. The applicant should also be familiar with online bioinformatics resources. The candidate will be responsible for processing and analyzing multi-scale omics data and leveraging cutting-edge methods to reconstruct disease models and generate in-silico hypothesis on drug targets. Accordingly the ideal candidate will have experience with genomic and transcriptomic data (and ideally with other omics data such as epigenomic, proteomic or metabolomic data), the different sequencing technologies / platforms as well as microarrays, read mapping and alignment, quality control protocols, variant calling, genome-wide association scans, expression quantitative trait analyses, co-expression analyses, biomarker discovery and similar analyses. Specifically the candidate will be expected to be familiar with sequencing data tools such as Picard tools, SAMtools, FastQC, aligners such as BWA, Tophat, STAR, variant calling pipelines such as GATK tools / Queue, SAMtools mpileup, annotation packages and resources such as Ensembl, GENCODE, BLAST, GSEA, mSigDB, GWAS catalog, OMIM, KEGG, online data resources such as GTEx, TCGA, GEO, dbGaP, genotype processing, QC and analysis tools such as PLINK, IMPUTE2, relevant R packages such as limma, edgeR, MatrixEqtl, RNAseq tools such as featureCounts, HTSeq, Cufflinks. The successful candidate must be able to work closely with our method development team and our national and international clinical, wet and computational lab collaborators.

For both profiles, the successful candidate will be supervised by Dr. Rui Chang and will have the opportunity to develop their own research ideas as they fit within the general research interests of our group.
The post is based at the main Mount Sinai campus, within the Icahn School of Medicine at Mount Sinai. General information about the Postdoctoral Training Program at Mount Sinai can be found at http://icahn.mssm.edu/education/postdoc. To learn more about the Icahn School of Medicine: http://icahn.mssm.edu.
Incoming postdoctoral fellows are eligible for affordable Mount Sinai Housing within walking distance of the medical school and of a wide range of amenities.
Mount Sinai Medical Center is an equal opportunity/affirmative action employer. We recognize the power and importance of a diverse employee population and strongly encourage applicants with various experiences and backgrounds.

Mount Sinai Medical Center–An EEO/AA-D/V Employer.
To apply, please send CV and cover letter to Rui Chang (rui.r.chang@mssm.edu).