美國紐約康奈爾醫(yī)學(xué)院胃腸道腫瘤轉(zhuǎn)化實(shí)驗(yàn)室2024年招聘博士后（計(jì)算遺傳組學(xué)/生物信息學(xué)）

康奈爾學(xué)院（Cornell College）是一所私立文理學(xué)院，位于美國艾奧瓦州芒特弗農(nóng)（Mount Vernon），建校于1853年。學(xué)校成立之初是一所神學(xué)院，名叫Iowa Seminary ，1856年，因?yàn)橐还P來自紐約的捐款，學(xué)校更名為Cornell College，以紀(jì)念William W. Cornell先生對學(xué)校的貢獻(xiàn)。學(xué)校的強(qiáng)項(xiàng)是法律、醫(yī)學(xué)預(yù)科、戲劇表演、環(huán)境科學(xué)以及經(jīng)濟(jì)和商科。

Employer

Maria Wright

Location

New York City, New York (US)

Salary

A competitive salary will be offered

Closing date

Sep 13, 2024

The Gastrointestinal (GI) Oncology Translational Lab at Weill Cornell Medicine in New York City is seeking a highly skilled computational biologist with a strong analytical background, driven to tackle the mysteries of biology with high-throughput data. We are looking for a bright and motivated person to join our research program to study tumor microenvironments, unique molecular disease subtypes, and their relation to resistance and sensitivity to cytotoxic therapy and immunotherapies. The Translational Lab (PI Manish A. Shah, MD) in the GI Oncology program at Weill Cornell is a multidisciplinary group focused on novel drug and immunotherapy development across GI malignancies (https://shahlab.weill.cornell.edu/). Drivers of resistance including disease subtypes, the tumor microenvironment, and host factors are explored in preclinical models and from blood and tissue samples across an array of patients treated on clinical trials.

As a computational genomic scientist in the GI translational lab, you will perform in-depth analyses of high-throughput data (single-cell of various modalities ,bulk RNA-seq, CITE-seq, ATAC-seq, ChIP-seq/CUT&TAG, and more ...) to address a multitude of biological or clinical questions at the forefront of research across GI Oncology. You will have access to resources and mentorship at the Weill Cornell computational biology community. The computational biology program at WCM is staffed by renowned faculty and researchers with extensive expertise in analyzing biological data from diverse types of high-throughput experiments, development of ML algorithms and building standardized workflows with an emphasis on thorough quality control and statistical rigor.

Central to this position is the ability to quickly develop a deep understanding of the underlying biology that each project addresses and of the scope and limitations of the individual experimental methods that are used. We are looking for candidates with deep knowledge of genomics, cell biology, and experimental protocols related to DNA sequencing and/or other ‘omics data types who are passionate about science and data analysis.

Qualifications we are looking for:

PHD in Statistical/Computational Genomics or Bioinformatics, with a strong background in the biological sciences. Expertise in Unix environment, shell scripting, high performance computing, cloud and cluster environments, and version control with git. Intimate knowledge of common high-throughput sequencing analysis tools such as short-read/sequence aligners (STAR, salmon, BWA...), single-cell analysis packages (Bioconductor, scanpy, Seurat...), visualization approaches, and functional annotation of sequencing data. Strong programming skills in at least one of Python, R, Julia; or compiled languages such as C/C++, Rust, and JAVA. Critical and creative thinking, detail-oriented work, enthusiasm to work on team projects, effective communication and presentation skills, never-ending curiosity, problem solving and passion for educating fellow staff, students and faculty about bioinformatics principles.

You will:

Be responsible for designing and carrying out bioinformatics and biostatistical analyses of next generation sequencing (NGS) datasets from a variety of platforms, including Illumina and 10X Genomics. This includes a strong focus on quality control and critical assessment of the experimental design as well as normalization, clustering, visualization, cellular annotation (for single-cell assays) and additional downstream analyses. Critically review, analyze, summarize, and communicate results as well as limitations of analyses to research faculty, staff, and clinical collaborators. Prepare results for publications, work with collaborators in writing publications and optimizing visualizations. Write robust code that is rigorously documented and liberally commented that that can easily be shared via code repositories. Organize data in a systematic, resource-conscious way. Curate metadata and upload data sets to public repositories. Actively participate in identifying, evaluating, and benchmarking new computational tools and methodologies that will meet the scientific goals of the GI oncology research. Educate faculty and lab members in contemporary bioinformatics methods and best practices via small group training and one-on-one consulting.

This position combines the best aspects of industry and academia; it is ideal for PhD graduates looking to work in a thriving and stimulating research environment on cutting-edge medical and clinical research projects, directly impacting multiple research efforts. The focus of our work is to understand the data at hand and how it relates to the initial research question. If you enjoy detail-oriented data exploration, hypothesis testing, writing robust code, and contributing to a wide range of research projects, then this job is for you!

To apply for this position, please email your CV in PDF format only along with a short cover letter to mas9313@med.cornell.edu

Manish A. Shah, MD

Chief, Solid Tumor Oncology, Hematology and Medical Oncology

Director Gastrointestinal Oncology Program, Hematology and Medical Oncology

Chair, GI Disease Management Team, Meyer Cancer Center

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