美国约翰霍普金斯大学计算精神病学遗传学博士后职位招聘

　　招聘简介：

　　计算精神病学遗传学

　　•约翰霍普金斯大学医学院

　　•地点：马里兰州巴尔的摩市

　　•工作号码：7055220

　　•发布日期：2019年1月11日

　　•申请截止日期：开放至填写

　　职位描述

　　博士后职位现在可以与约翰霍普金斯大学精神病学和行为科学系副教授Shizhong Han博士一起工作。

　　韩博士实验室的研究重点是研究精神疾病的遗传基础，并开发统计遗传学方法和计算工具，以促进更广泛地识别疾病易感基因的努力。

　　该候选人预计将与韩博士合作开发和应用机器学习和基于网络的方法，以进行风险基因鉴定，疾病风险预测和精神疾病的药物发现。研究项目将包括：1）整合多组学数据集，包括GWAS，RNA-Seq和Chip-Seq，以确定几种主要精神疾病的致病基因和非编码变异; 2）治疗反应的生物标志物发现; 3）个性化风险基因推断; 4）药物再利用治疗精神疾病。将考虑具有人类遗传学，生物信息学或具有强定量技能（例如，生物统计学，统计学或计算机科学）的其他领域的博士学位的候选人。

　　英文原文：

　　Computational Psychiatric Genetics

　　·         Johns Hopkins School of Medicine

　　·         Location: Baltimore, MD

　　·         Job Number: 7055220

　　·         Posting Date: Jan 11, 2019

　　·         Application Deadline: Open Until Filled

　　Job Description

　　A postdoc position is now available to work with Dr. Shizhong Han, Associate Professor, in the Department of Psychiatry and Behavioral Sciences at Johns Hopkins.

　　Research in Dr. Han’s lab has been focused on investigating the genetic basis of psychiatric disorders, and developing statistical genetic approaches and computational tools that will facilitate the effort to identify disease susceptibility genes more broadly.

　　The candidate is expected to work with Dr. Han to develop and apply machine learning and network-based approaches to risk gene identification, disease risk prediction, and drug discovery for psychiatric disorders. Research projects will include: 1) integration of multi-omics datasets including GWAS, RNA-Seq and Chip-Seq to identify causal genes and noncoding variants underlying several major psychiatric disorders; 2) biomarker discovery for treatment response; 3) personalized risk gene inference; and 4) drug repurposing for psychiatric disorders. Candidates with a doctoral degree in human genetics, bioinformatics, or other fields with strong quantitative skills (e.g., biostatistics, statistics, or computer science) will be considered.

　　Strong programing skills in Perl/Python and R are essential. Experiences with large-scale genetic or genomic datasets are necessary. Experiences with network-based approach or machine learning are a plus. The candidates need to be highly motivated, excellent in academic writing, and able to work well in a collaborative environment.

　　The successful candidate will receive intensive training on projects tailored to his/her research interests and experiences. The successful candidate will be encouraged to present research work in national and international meetings, and be supported for professional development.

　　Interested applicants should send curriculum vitae, statement of research interests, and contact information for three references to Shizhong Han at: shan67[at]jhmi[dot]edu.