Postdoctoral Fellow in Bioinformatics

We are seeking a highly qualified Bioinformatics expert with software engineering skills to develop and implement bioinformatics/biostatistics methodologies for clinical outcome prediction, disease classification, and therapeutic target discovery using large-scale genomics data generated by microarray and sequencing. Successful candidate will closely interact with experienced bioinformatics, biostatistics, and software engineering experts at the Institute of Genomics and Multiscale Biology. The lab is a part of the Tisch Cancer Institute in the newly launched Center for Science and Medicine building, housing cutting-edge research facilities and world-class scientists, located in Upper East Side of Manhattan.

Requirements:

- Highly motivated PhD in Statistics, Computer Science, Bioinformatics, or other relevant areas.

- Significant familiarity with standard machine learning and statistical modeling algorithms.

- Strong programing skills with a statistical computing language preferably R.

- Experience of high-throughput genomic data analysis.

- Strong software engineering skills and experience (Java, SQL, web application).

- Quick learner of new technologies and analysis algorithms.

- Strong interpersonal skills and a track record of collaborative work in multidisciplinary research environment.

To apply, e-mail a CV, a cover letter, URL of web application or database (if any), and contact information of 3 references to:

Yujin Hoshida, MD, PhD (yujin.hoshida@mssm.edu)

Assistant Professor of Medicine

Liver Cancer Program, Tisch Cancer Institute

Division of Liver Diseases, Department of Medicine

Icahn School of Medicine at Mount Sinai

1470 Madison Ave, New York, NY 10029

References:

1. “Gene expression in fixed tissues and outcome in hepatocellular carcinoma”, N Engl J Med 359;1995-2004, 2008

2. “Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma”, Gastroenterology 140;1501-12, 2011

3. “Gene signatures in the management of hepatocellular carcinoma”, Semin Oncol 39;473-85, 2012

4. “Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma”, Cancer Res 69;7385-92, 2009

5. “Lin28 promotes transformation and is associated with advanced human malignancies”, Nature Genet 41;843-8, 2009

6. “Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment.”, PLoS One 5;e15543, 2010

7. “Subclass mapping: identifying common subtypes in independent disease data sets”, PLoS One 2;e1195, 2007

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