Cancer Computational Biologist (Postdoctoral Fellow)

This position offers a unique opportunity to contribute collaboratively to cutting-edge research in the field of cancer therapeutics and precision medicine that spans both basic and translational science objectives. The successful candidates will develop novel bioinformatic machine learning methodologies to: 1) explore cancer-specific genomic, epigenetic, and metabolic alterations, molecular systems pharmacology, and network biology and 2) understand molecular mechanisms of drug response and drug resistance to achieve precision medicine. These strategies will aim to understand the fundamental rules for how cells respond to external perturbation. In addition, selected applicants will have the opportunity to contribute to the translational clinical program of the NCI, aiming to support clinical decision-making by exploring cancer heterogeneity using both pre-clinical models (e.g., cell lines, patient-derived xenografts, organoids) and patient datasets, identifying biomarkers and pinpointing the molecular determinants of response to cancer therapy. We are offering full-time postdoctoral fellow positions, available immediately and renewable on a yearly basis. The NIH offers a competitive salary and comprehensive health insurance. Initial appointments will be for 1 year, with possible extensions up to 5 years. The NIH is dedicated to building a diverse community in its training and employment programs as well as the continued education and career development of all its research staff. These positions are subject to background checks.

Related Articles

• Causal Interactions: Molecular Data Meets Pathways: https://pubmed.ncbi.nlm.nih.gov/34179843/
• Understanding Drug Resistance through Network Data: https://pubmed.ncbi.nlm.nih.gov/36103824/
• CellMiner Cross-Database for Pharmacogenomics: https://pubmed.ncbi.nlm.nih.gov/33196823/
• scPerturb: Harmonized Single-Cell Perturbation Data: https://www.biorxiv.org/content/10.1101/2022.08.20.504663v3

Additional Links

• https://ccr.cancer.gov/staff-directory/augustin-luna
• https://www.nlm.nih.gov/research/researchstaff/LunaAugustin.html
• Google Scholar: https://scholar.google.com/citations?user=u2dgLp8AAAAJ&sortby=pubdate
• Summary of NIH as a Training Environment (Reputation, Cost of Living, Social Climate): https://github.com/cannin/nih

About the NLM The National Library of Medicine (NLM, https://www.nlm.nih.gov/research/index.html) pioneers new ways to make biomedical data and information more accessible; and builds tools for better data management and personal health. NLM’s cutting-edge research and training programs (with a focus on artificial intelligence (AI), machine learning, computational biology, and biomedical informatics and health data standards) help catalyze basic biomedical science, data-driven discovery, and health care delivery.

About the NCI/CCR/DTB The National Cancer Institute Center for Cancer Research (NCI-CCR, https://ccr.cancer.gov/) is the largest division of the NCI; it encompasses various branches such as the NCI Developmental Therapeutics Branch. The NCI CCR has a mandate to confront the special challenges presented by rare cancers as well as cancers that may be predominant in medically underserved populations. One way in which the NCI CCR addresses this mandate is by conducting clinical trials that recruit patients with rare cancers thereby generating unique data to advance research in these cancers. While rare cancers affect low numbers of patients, as a group, they account for about a quarter of all cancers, as well as a quarter of all cancer deaths each year (https://www.cancer.gov/pediatric-adult-rare-tumor/rare-tumors/about-rare-cancers).

Essential

• PhD in a relevant field, including: Statistics, Mathematics, Data Science, Physics, Engineering, or Computer Science, or a degree related to Biology with substantial experience in computational and statistical work. Individuals in the final stages of PhD submission will be considered as well as PhD graduates within 5 years of graduation.
• Strong knowledge and experience in coding (R/Python or similar languages; the lab has a significant, existing codebase in R)
• Prior and demonstrated experience analyzing -omics data (e.g., RNAseq, copy number, mutation, methylation, proteomics)
• Technical expertise in machine learning and/or mathematical modeling
• An interest in applying computational methods to biological problems
• A demonstrated ability to generate and pursue independent research ideas
• Excellent communication skills, written and verbal as evidenced by publications, preprints, and/or conference presentations
• Dedication to reproducible research and open science

Desirable

• Foundational knowledge in Bioinformatics, Systems Biology, and/or similar fields
• Foundational knowledge in Mathematics, Statistics, and/or Data Science
• Familiarity with software development practices and high-performance computing
• Experience with machine learning frameworks (Pytorch/Tensorflow or similar)
• Experience with mass spectrometry-based proteomics data analysis is highly desirable
• Experience with using network-based analyses (graph theory) and software/resources (graph and/or pathway databases) is highly desirable
• Experience with single-cell data analysis is highly desirable
• Experience working in collaborative interdisciplinary environments

The NIH is dedicated to building a diverse community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities.