招聘简介：

博士后奖学金，计算和统计癌症基因组学

职位描述：

国家癌症研究所病理学实验室正在寻找癌症基因组学和表观基因组学项目的博士后研究员。我们研究多种癌症类型，重点是原发性人脑胶质瘤（对于我们已发表的研究，请参阅https://www.ncbi.nlm.nih.gov/pubmed/?term=aldape+k）。该实验室的首要目标是利用计算技能从大规模多元数据中解释生物模式，最终目标是根据生物数据诊断和细分人类癌症。

成功的申请人将参与开发新的统计和遗传方法，理论和计算工具以及机器学习策略，用于分析和整合基因组和表观基因组数据与人类癌症的病理评估。获得广泛的核心设施，包括生物信息学，微阵列，细胞基因组学，测序和病理图像存储库的核心，为机器学习和深度学习计算方法的发展提供了令人兴奋的机会。职业发展机会广泛，旨在提供与各种学科和生物医学科学的领先专家互动的机会，并培养候选人成为全面的独立调查员，为未来的发展做好准备。

英文原文：

Post-Doctoral Fellowship, Computational and Statistical Cancer Genomics

Position Description:

Laboratory of Pathology of National Cancer Institute is looking for postdoctoral researcher to work on cancer genomics and epigenomics project. We study variety of cancer types with focus on primary human brain gliomas (for our published work, please see https://www.ncbi.nlm.nih.gov/pubmed/?term=aldape+k). The overarching goal of the lab is to use computational skills to interpret biological patterns from large scale multi-omic data with ultimate goal to diagnose and subclassify human cancer accordingly to biological data.

The successful applicant will be involved in development of new statistical and genetic methods, theory and computational tools and machine learning strategies for analysis and integration of genomic and epigenomic data with pathologic evaluation of human cancer. Access to extensive core facilities including cores in bioinformatics, microarrays, cytogenomics, sequencing and pathological image depositories provides exciting opportunities for development of machine learning and deep learning computational approaches. Career development opportunities are extensive and aimed to provide the opportunity to interact with leading experts in a range of disciplines and biomedical science, and to develop candidates into well rounded, independent investigators ready for future advancement.

The NIH has a rich research environment with countless opportunities for collaboration and cutting-edge technology resources for biomedical research. Laboratory of Pathology (LP) actively interacts with the Cancer Data Science Laboratory (CDSL) in the intramural program, led by Eytan Ruppin (https://www.ncbi.nlm.nih.gov/pubmed/?term=ruppin+e). His group in the CSDL specializes in a variety of computational approaches, analyzing and integrating cancer multi-omics data to better understand cancer biology, classification, and new therapeutic options for patients. Access to the resources unique to the LP as well as to the CDSL, will provide state-of-the-art career development opportunities for computational biologists. Both experts in cancer molecular classification (Dr. Kenneth Aldape, Chief of LP) and cancer multi-omics data integration (Dr. Eytan Ruppin, Chief of the CDSL) are committed to co-mentor and advance independent investigators of the future.

The Laboratory of Pathology within the intramural program of the NCI, NIH, is located in Bethesda, Maryland, USA. Its mission is to achieve the highest level of quality in clinical diagnostics, cancer research and education. Training programs are made available through the NCI Training Office and the NIH Office of Intramural Training and Education. The NIH is dedicated to building a diverse community in its training and employment programs.

Qualifications:

The successful candidate will have a strong quantitative research background with a PhD in areas such as Statistical Genetics, Statistical Methods, Machine Learning, Deep Learning or Computer Science; practical experience working with large real-world genetic data sets, developing new methods, statistical computing, and producing high-quality published work. We expect the candidate to be motivated, creative, communicative and happy to work in a collaborative and inter-disciplinary environment.

To Apply:

Suitably qualified candidates should submit their curriculum vitae, cover letter, and the names and contact information (email and phone #) for three referees to:

Dr. Zied Abdullaev

Laboratory of Pathology

Center for Cancer Research

National Cancer Institute

Bethesda, MD 20892

United States

zied.abdullaev@nih.gov