弗吉尼亚理工大学Dr. Mei实验室诚聘生物信息学博士研究生和博士后
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弗吉尼亚理工大学Dr. Mei实验室诚聘生物信息学全奖博士生、博士后
梅胜林课题组依托于弗吉尼亚理工大学FBRI癌症研究中心及华盛顿特区儿童国家医学中心建立。课题组致力于研究肿瘤微环境在肿瘤侵袭和转移过程中的重塑和调控机制。实验室结合单细胞和空间转录组测序技术、多组数据集、机器学习和实验方法，探讨肿瘤微环境及基因调控因子如何控制肿瘤转移。梅胜林博士毕业于同济大学/哈佛大学刘小乐教授课题组，并在哈佛医学院Peter Kharchenko和麻省总院David Sykes实验室接受博士后训练，以第一/共一/共同通讯作者在Cancer Cell, Nature methods, Nat Commun, Journal of Hepatology, Genome Medicine等期刊发表论文14篇，获得PCF Young Investigator Award 。欢迎具有生物信息学、生物学、计算机、物理、统计等相关专业的博士生和访问学者加盟。课题组位于首都华盛顿特区北部，气候宜人，安全舒适，交通便利。
Dr. Shenglin Mei lab (shenglinmei.github.io ) at Fralin Biomedical Research Institute (FBRI) Cancer Research Center DC of Virginia Tech, and Children's National Center for Cancer and Immunology Research, is seeking highly motivated postdoctoral fellow and graduate students to join our team. The Mei Laboratory studies the remodeling and regulatory mechanisms of the tumor microenvironment during tumor progression and metastasis. The lab specializes in computational biology, and combines single-cell technologies, genomic datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding of malignant cell heterogeneity and context-dependent tumor microenvironment remodeling, we aim to drive the development of novel therapeutic approaches for patients with metastatic cancer. The current focuses in lab include the following directions: 1) Developing computational methods for integrating multi-modal data, such as scRNA-seq, scATAC-seq, spatial transcriptomics, ChIP-seq, and CRISPR screening. 2) Investigating context-dependent remodeling of the tumor microenvironment. 3) Identifying novel regulators within the tumor microenvironment. 4) Exploring tumor organ specific metastasis with multi-omics data integration.
The candidate will develop novel statistical and machine learning methods for multi-omics data integration and apply the computational methods to explore the dysregulation and regulation of tumor microenvironment, malignant cell heterogeneity, plasticity, and tumor immunology.
How to apply
Interested applicants should submit the following materials via email to Dr. Shenglin Mei (1point3acres.com): 1) a cover letter describing current research activities and your interest in this position, 2) a current CV (including publications).
梅胜林课题组依托于弗吉尼亚理工大学FBRI癌症研究中心及华盛顿特区儿童国家医学中心建立。课题组致力于研究肿瘤微环境在肿瘤侵袭和转移过程中的重塑和调控机制。实验室结合单细胞和空间转录组测序技术、多组数据集、机器学习和实验方法，探讨肿瘤微环境及基因调控因子如何控制肿瘤转移。梅胜林博士毕业于同济大学/哈佛大学刘小乐教授课题组，并在哈佛医学院Peter Kharchenko和麻省总院David Sykes实验室接受博士后训练，以第一/共一/共同通讯作者在Cancer Cell, Nature methods, Nat Commun, Journal of Hepatology, Genome Medicine等期刊发表论文14篇，获得PCF Young Investigator Award 。欢迎具有生物信息学、生物学、计算机、物理、统计等相关专业的博士生和访问学者加盟。课题组位于首都华盛顿特区北部，气候宜人，安全舒适，交通便利。
Dr. Shenglin Mei lab (shenglinmei.github.io ) at Fralin Biomedical Research Institute (FBRI) Cancer Research Center DC of Virginia Tech, and Children's National Center for Cancer and Immunology Research, is seeking highly motivated postdoctoral fellow and graduate students to join our team. The Mei Laboratory studies the remodeling and regulatory mechanisms of the tumor microenvironment during tumor progression and metastasis. The lab specializes in computational biology, and combines single-cell technologies, genomic datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding of malignant cell heterogeneity and context-dependent tumor microenvironment remodeling, we aim to drive the development of novel therapeutic approaches for patients with metastatic cancer. The current focuses in lab include the following directions: 1) Developing computational methods for integrating multi-modal data, such as scRNA-seq, scATAC-seq, spatial transcriptomics, ChIP-seq, and CRISPR screening. 2) Investigating context-dependent remodeling of the tumor microenvironment. 3) Identifying novel regulators within the tumor microenvironment. 4) Exploring tumor organ specific metastasis with multi-omics data integration.
The candidate will develop novel statistical and machine learning methods for multi-omics data integration and apply the computational methods to explore the dysregulation and regulation of tumor microenvironment, malignant cell heterogeneity, plasticity, and tumor immunology.
How to apply
Interested applicants should submit the following materials via email to Dr. Shenglin Mei (1point3acres.com): 1) a cover letter describing current research activities and your interest in this position, 2) a current CV (including publications).
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