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美国约翰霍普金斯大学计算精神病遗传学博士后职位招聘
文章来源:知识人网整理       更新时间:2019年03月14日

  招聘简介:

  计算精神病学遗传学

  ·约翰霍普金斯医学院

  ·地点:马里兰州巴尔的摩

  ·工作编号: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

  现在有一个博士后职位可以与约翰霍普金斯大学精神病学和行为科学系的副教授韩世忠博士合作。

  在韩博士的实验室里,研究的重点是调查精神疾病的遗传基础,并开发统计遗传方法和计算工具,以便更广泛地识别疾病易感性基因。

  该候选人将与韩博士合作,开发并应用机器学习和基于网络的方法来识别风险基因、预测疾病风险以及发现精神疾病的药物。研究项目将包括:1)整合包括GWAS、RNA-Seq和Chip-Seq在内的多组学数据集,以确定几种主要精神疾病的致病基因和非编码变体;2)治疗反应的生物标记物发现;3)个性化风险基因推断;以及4)精神疾病的药物再利用。将考虑具有人类遗传学、生物信息学或其他具有很强定量技能(如生物统计学、统计学或计算机科学)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.

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