Join a dynamic and interdisciplinary team pushing the frontiers of single cell genomics and machine learning under the leadership of Dr. Yun (Renee) Zhang in the Division of Intramural Research (DIR) at the National Library of Medicine (NLM).

ABOUT THE POSITION

The NLM is one of the 27 institutes at the National Institutes of Health (NIH). The NLM is the world’s largest biomedical library and a leader in research, development, and training in biomedical informatics and health information technology. The DIR within the NLM has two primary research areas: computational health research and computational biology. In computational health research, our efforts center on natural language processing (NLP), clinical image analysis, biomedical ontologies, information modeling, and clinical data analytics. In computational biology, we emphasize transcriptional regulation, chromatin and network biology, structural and functional analysis, sequence statistics, and evolutionary genomics.
The Zhang lab is interested in the development and application of novel computational methods based on machine learning and advanced biostatistics/statistics techniques to analyze large-scale multi-omics single cell data from human and other species in health and disease and to identify data-driven biomarkers for disease diagnostics and therapeutics. Our group conducts “dry lab” computational research and closely collaborates with “wet lab” investigators to understand and characterize the diverse cell phenotypes at single cell resolution utilizing various single cell genomics approaches.
We are looking for a highly motivated Postdoctoral Fellow working in the exciting single cell genomics field. Projects will focus on various integrative analyses of single cell multi-omics data, including single cell/nucleus RNA-seq (scRNA-seq) data, spatial transcriptomics data, etc. These projects will establish rigorous computational strategies for enhancing the data analysis pipelines and extracting trustworthy knowledge, contributing to the NLM resource of the Cell Knowledge Network.

Specific tasks and responsibilities include:

• Lead methodology development using machine learning and advanced statistical approaches for single cell genomics analysis.
• Identify and translate the real data problems encountered in various use cases of the off-the-shelf machine learning algorithms into modeling frameworks that can be assessed using fundamental techniques of data science and statistical theory.
• Construct innovative solutions to address analytical challenges with novel metrics and methods.
• Collaborate with internal and external collaborators and domain experts in the scope of larger collaborative projects.
• Communicate method development, model evaluation, real data analysis, and biological findings to technical and non-technical audiences.
• Keep up to date with the latest literature of the related domains.
• Provide data intelligence support to NLM Cell Knowledge Network team.

Position Overview: This is a full-time postdoctoral fellow position. The initial appointment will be for one year, and is renewable on a yearly basis, with extensions up to 5 years total. The NIH offers a competitive salary (based on postdoctoral experience, see stipend tables: https://www.training.nih.gov/stipends/ ) and comprehensive health insurance. The NIH is dedicated to the continued education and career development of all its research staff. Candidates are subject to a background investigation. Additional information about NIH postdoctoral fellowships: https://www.training.nih.gov/research-training/pd/
What you’ll need to apply
Prospective candidates should include “Postdoc Inquiry” and their last name in the email subject line.

Applicants must submit the following materials to Dr. Zhang at yun.zhang@nih.gov :

• Updated CV
• Cover letter with a short research statement and preferred starting date
• Link(s) to published artifacts (packages, GitHub repositories, etc.), and
• Contact information for 3 references (please include the full name with titles, institute, email address and phone number of each reference).

Application Deadline: Applications will be accepted and reviewed on a rolling basis until the position is filled.
Contact name
Dr. Zhang
Contact email
yun.zhang@nih.gov

QUALIFICATIONS

• Doctorate degree in statistics, biostatistics, bioinformatics, computational biology, or in a related biomedical science field.
• Expertise in high dimensional data analysis, statistical inference, supervised and unsupervised machine learning.
• Publishing experience with peer-reviewed journals in the related fields. • Programming proficiency in Python and R.
• Working knowledge of software development tools, e.g., GitHub, Docker.
• Working knowledge of Linux system and high-performance computing (HPC).
• Excellent interpersonal skills, including oral and written communication, and the ability to work in the cross-functional groups.
• A demonstrated ability to generate and pursue independent research ideas.

• Lead methodology development using machine learning and advanced statistical approaches for single cell genomics analysis.
• Identify and translate the real data problems encountered in various use cases of the off-the-shelf machine learning algorithms into modeling frameworks that can be assessed using fundamental techniques of data science and statistical theory.
• Construct innovative solutions to address analytical challenges with novel metrics and methods.
• Collaborate with internal and external collaborators and domain experts in the scope of larger collaborative projects.
• Communicate method development, model evaluation, real data analysis, and biological findings to technical and non-technical audiences.
• Keep up to date with the latest literature of the related domains.
• Provide data intelligence support to NLM Cell Knowledge Network team