Join US
We have open positions for enthusiastic postdoctoral fellows and research assistants who are passionate about bridging computational sciences and cancer biology.
Postdoctoral Fellows:
Current projects include developing and applying statistical machine learning and deep learning techniques for analyzing and integrating high-dimensional single-cell and spatial genomic and imaging datasets to understand tumor heterogeneity and tumor-immune interactions. Ideal candidates have a background in machine learning, statistics, computer science, bioinformatics and/or genomics, cancer biology, immunology. We have opportunities for both computational and experimental research. Interested candidates should email Elham (ea2690 AT columbia DOT edu) with their CV, a summary of research interests and future plans, and contact information for three references.
Postdoctoral research scientist position at Columbia University and NYGC
with Professors David A Knowles and Elham Azizi
We are jointly seeking a postdoctoral research scientist with a background in computational biology, machine learning, and statistics, as well as preferably genomics and cancer biology. Our collaborative projects combine causal inference and gene regulatory network modeling underlying cancer progression, metastasis and drug resistance.
The Knowles lab (https://daklab.github.io/) aims to understand the role of transcriptomic dysregulation across the spectrum from rare to common genetic disease. This involves better characterization of the genetic and environmental factors contributing to mRNA expression and splicing variation. Beyond this specific project there are opportunities for close collaboration with diverse research groups at NYGC collecting large-scale genomics datasets and developing novel genomic technologies including single cell methods, forward genetic screens and long-read transcriptomics. The lab is joint between the New York Genome Center (NYGC) and Columbia University Departments of Computer Science and Systems Biology
The Azizi Lab (azizilab.com) utilizes an interdisciplinary approach combining cutting-edge single-cell genomic and imaging technologies with statistical machine learning techniques, to characterize complex populations of interacting cells in the tumor microenvironment as well as their dysregulated circuitry and spatial organization. The Azizi lab is primarily affiliated with the Biomedical Engineering Department and the Irving Institute for Cancer Dynamics (IICD) at Columbia University. We are also affiliated with the Computer Science Department, Data Science Institute and the Herbert Irving Comprehensive Cancer Center. The lab is located in the Columbia Morningside (main) campus.
Minimum Degree Required: PhD in Computer Science, Biomedical Engineering, Bioinformatics, Computational Biology, Biology, Statistics or other relevant fields.
Qualifications:
Graduate Students:
Prospective students can apply through the Biomedical Engineering or Computer Science programs. Please send your CV and a summary of your research experience and interests to Elham (ea2699 AT columbia.edu).
Undergraduate Students:
Motivated undergraduate students currently enrolled at Columbia are welcome to join the lab. Email Elham (ea2690 AT columbia DOT edu) with your CV, major, and research interests.
Summer internships:
High school and undergraduate students interested in summer research internships can apply to the IICD program, AI4ALL and NY Bioforce. Students from traditionally underrepresented groups in STEM fields are strongly encouraged to apply.
Postdoctoral Fellows:
Current projects include developing and applying statistical machine learning and deep learning techniques for analyzing and integrating high-dimensional single-cell and spatial genomic and imaging datasets to understand tumor heterogeneity and tumor-immune interactions. Ideal candidates have a background in machine learning, statistics, computer science, bioinformatics and/or genomics, cancer biology, immunology. We have opportunities for both computational and experimental research. Interested candidates should email Elham (ea2690 AT columbia DOT edu) with their CV, a summary of research interests and future plans, and contact information for three references.
Postdoctoral research scientist position at Columbia University and NYGC
with Professors David A Knowles and Elham Azizi
We are jointly seeking a postdoctoral research scientist with a background in computational biology, machine learning, and statistics, as well as preferably genomics and cancer biology. Our collaborative projects combine causal inference and gene regulatory network modeling underlying cancer progression, metastasis and drug resistance.
The Knowles lab (https://daklab.github.io/) aims to understand the role of transcriptomic dysregulation across the spectrum from rare to common genetic disease. This involves better characterization of the genetic and environmental factors contributing to mRNA expression and splicing variation. Beyond this specific project there are opportunities for close collaboration with diverse research groups at NYGC collecting large-scale genomics datasets and developing novel genomic technologies including single cell methods, forward genetic screens and long-read transcriptomics. The lab is joint between the New York Genome Center (NYGC) and Columbia University Departments of Computer Science and Systems Biology
The Azizi Lab (azizilab.com) utilizes an interdisciplinary approach combining cutting-edge single-cell genomic and imaging technologies with statistical machine learning techniques, to characterize complex populations of interacting cells in the tumor microenvironment as well as their dysregulated circuitry and spatial organization. The Azizi lab is primarily affiliated with the Biomedical Engineering Department and the Irving Institute for Cancer Dynamics (IICD) at Columbia University. We are also affiliated with the Computer Science Department, Data Science Institute and the Herbert Irving Comprehensive Cancer Center. The lab is located in the Columbia Morningside (main) campus.
Minimum Degree Required: PhD in Computer Science, Biomedical Engineering, Bioinformatics, Computational Biology, Biology, Statistics or other relevant fields.
Qualifications:
- Publication record in computational biology, machine learning, or statistics
- Strong interest in applications of statistical machine learning in genomics
- Highly independent and interested in working with a multidisciplinary team
- Strong programming skills in Python or R
- Technical skills and coursework in machine learning, data science or statistics
- Previous experience in developing novel machine learning methods or computational biology tools
- Experience with processing and analysis of genomic data and knowledge of cancer biology is preferrable
Graduate Students:
Prospective students can apply through the Biomedical Engineering or Computer Science programs. Please send your CV and a summary of your research experience and interests to Elham (ea2699 AT columbia.edu).
Undergraduate Students:
Motivated undergraduate students currently enrolled at Columbia are welcome to join the lab. Email Elham (ea2690 AT columbia DOT edu) with your CV, major, and research interests.
Summer internships:
High school and undergraduate students interested in summer research internships can apply to the IICD program, AI4ALL and NY Bioforce. Students from traditionally underrepresented groups in STEM fields are strongly encouraged to apply.
