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| 【有奖交流】积极回复本帖子，参与交流，就有机会分得作者 mellowmeadow 的 8 个金币 ，回帖就立即获得 2 个金币，每人有 1 次机会 | |||
[交流]
Computational Postdoc positions in Machine Learning Computational Biology at USA
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Dr. Fan Zhang, PhD, Assistant Professor, PhD, Assistant Professor of Department of Medicine and the Center for Health AI (Artificial Intelligence) at the University of Colorado School of Medicine is looking for talented computational Postdoc fellow, bioinformaticians, and graduate students. The Zhang lab https://fanzhanglab.org/ focuses on developing and using statistical machine learning methods, single-cell multi-omics, and systems immunology to study inflammatory diseases. Dr. Zhang has published first-authored papers in Nature Immunology, Genome Medicine, BMC Bioinformatics, etc and co-author papers in Nature, Nature Method, New England Journal of Medicine, Science Translational Medicine, Nature Communications, and Bioinformatics, etc. Dr. Zhang is also the Reviewer for Nature, Nature Medicine, PNAS, Nature Communications, Bioinformatics, etc. Before joining the University of Colorado as faculty, Dr. Zhang did her Postdoc research (2017-2021) at Harvard Medical School, Broad Institute of Harvard and MIT, and Brigham and Women's Hospital. Job responsibility: The successful candidate will work on an established project focusing on analyzing newly generated large-scale single-cell mass cytometry (CyTOF) (~20 millions single cells) and single-cell RNA-seq datasets from hundreds of human patients with at-risk rheumatoid arthritis (RA), established RA, and SLE through ongoing collaborative efforts. Besides analyzing and integrating primary single-cell multi-omics data, the candidate will be in an outstanding position to develop computational and statistical tools to solve disease-driven computational problems, e.g. 1) how to associate and integrate CyTOF with single-cell RNA-seq from the same patients, 2) how to more accurately identify pathogenic features that correlate with disease progression from our longitudinal single-cell data, 3) identify which immune cell phenotypes migrate from blood into inflamed tissue, 4) how to associate serology and clinical features with cellular and molecular profiles, etc. Dr. Zhang is also an Investigator involved in the NIH funded Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium. The successful candidate will have the opportunity to make impactful contributions in a fast-paced translational research environment at the cutting-edge of computational biology, single-cell multi-omics, and novel disease-driven immunological findings. Required (minimum) qualifications: Postdoc fellow position: • PhD, or MD/PhD (or graduate soon) in the field of computational biology, systems biology, biostatistics or bioinformatics, statistical genomics, computer science, or other quantitative biomedical sciences • Fluent in R or Python, familiar with working in Linux and bioinformatics reproducibility tools using high-performance computing (HPC) • Salary: $53,760 - $70,000/year depends on expertise and experience Bioinformatician position: • Master degree in Bioinformatics, Biostatistics, Computational Biology, Systems Immunology, Biomedical Engineering or related quantitative biomedical sciences • Fluent in R or Python, familiar with working in Linux and bioinformatics reproducibility tools using high-performance computing (HPC) • Salary: $52,000 - $65,000/year depends on expertise and experience Graduate student: • Welcome PhD graduate students to apply! • We will also consider offering satisfactory salary depending on the expertise and experience of the PhD students Desired qualifications: • A strong background in at least one of the fields of machine learning, statistical modeling, systems biology, and high-dimensional sequencing data analysis • Programming experience (e.g. R or Python) in solving specific biological problems by implementing machine learning methods, including standard dimensionality reduction methods (e.g. principal component analysis, matrix factorization, manifold learning), classical generalized linear mixed model (GLMM, e.g. lme4), standard statistical tests (e.g. ANOVA, likelihood ratio test), canonical correlation analysis, and deep learning (e.g. variational autoencoder, Bayesian deep learning) • Familiar with standard single-cell RNA-seq or other single-cell omics data analysis pipeline, including quality control, dimensional reduction and clustering, batch effect correction, etc • Passion for developing novel analytical and computational methods, such as single-cell multimodal integration for clinical samples • A track record of scientific productivity and paper publication • Ability to thrive in a dynamic research environment and work independently and collaboratively as part of an interdisciplinary scientific team • Ideal candidates with demonstrated experience in any of the following areas will be given priority: CyTOF or single cell transcriptomic data analysis, multi-omics single cell data integration, traditional bulk RNA-seq data analysis, or development of statistical methods for genomics data modeling Ideal candidates can work remotely depends on specific situations! Required application: Please send application to Dr. Fan Zhang fanzhanglab@gmail.com with email title in the format of “Application-Zhang Lab-Your Name”. Please combine the materials below into one PDF file in this order: • Cover letter which addresses job requirements and outlines qualifications (limited to 1 page) • Curriculum vitae • A brief statement of research interests (limited to two pages) • 3 references with name, email, daytime telephone number, and address • Links to 1-2 your best papers if applicable • Links to 1-2 your source code repositories if applicable |
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