Postdoctoral Research Fellow, Microbiome Analysis Core | Harvard University at Harvard Universi[...]

kozmetickesluzby.vecnakraska.sk - Jobboard; Cambridge (MA); On-site; USD 70,000 - 90,000; Full time

Job summary

The Harvard T.H. Chan School of Public Health is seeking a data analyst for their Microbiome Analysis Core. This role involves applying microbiome informatics and statistical methods in studies related to health and disease. Candidates should have an MSc or Ph.D. in relevant fields, proficiency in R programming, and experience with microbiome analysis.

Excellent communication skills and attention to detail are essential for this position. The role requires collaboration with various stakeholders including scientists and clinicians.

Qualifications

• MSc or Ph.D. degree in Biostatistics, Bioinformatics, Computer Science, or related fields.
• Proficiency in R programming and Linux/Unix command line.
• Experience in microbiome analysis and related statistical methods.
• Excellent oral and written communication skills.
• Ability to effectively solve complex problems and shift priorities.

Responsibilities

• Apply microbiome informatics and statistical methods for population studies.
• Analyze human microbiome profiles and their associations with health.
• Collaborate with internal and external scientists and clinicians.

Skills

R programming

Linux/Unix command line

Microbiome analysis

Ordination and cluster analysis

Research excellence

Excellent communication skills

Problem-solving skills

Attention to detail

Education

MSc or Ph.D. in Biostatistics or related fields

Tools

Computing clusters (e.g. Slurm)

Job description

School: Harvard T.H. Chan School of Public Health

Department/Area: Biostatistics

Position Description:

The Harvard T.H. Chan School of Public Health Microbiome Analysis Core is seeking a data analyst, either MSc or Ph.D. level, for microbiome epidemiology and bioinformatics. The Microbiome Analysis Core, located in the Department of Biostatistics, supports a comprehensive computational and statistical platform for population studies of the human microbiome, its interaction with health and disease, and methods for data mining and machine learning in multi-omic data. This job will entail work with the Microbiome Analysis Core personnel applying and extending microbiome informatics and statistical methods, developed in the Huttenhower lab (e.g. MetaPhlAn, HUMAnN) as well as standards in the field (e.g. DADA2), to human microbiome profiles, including microbial communities assayed in disease, animal models, cross-sectional and prospective human cohorts, and associated clinical phenotypes and/or environmental/lifestyle exposure metadata. These studies generally have the goal of identifying features of the microbiome (16S amplicon, shotgun metagenomic, and shotgun metatranscriptomic sequencing, yielding taxa, gene families, enzymes, and/or pathways) associated with various phenotypes, exposures, and/or outcomes. There will be regular interactions with internal and external contacts, including scientists, collaborators, postdocs, students, and clinicians and industry leaders.

Basic Qualifications:

• MSc or Ph.D. degree in Biostatistics, Bioinformatics, Computer Science, Computational Biology, Molecular Biology, Biology/Life Sciences, or related fields.
• Proficiency in R programming and Linux/Unix command line.
• Preference given to candidates with experience in microbiome analysis, ordination and cluster analysis, sequence analysis, intermediate R programming, a background in biostatistics, and computing clusters (e.g. Slurm).
• Excellence in research.
• Excellent oral and written communication skills.
• Ability to handle a variety of tasks, effectively solve problems with numerous and complex variables, and rapidly shift priorities.
• Excellent attention to detail is required.

Contact Information:

Nicole Levesque

Contact Email: [e-mail]

Special Instructions:

Email application to [e-mail] and [e-mail]

Equal Opportunity Employer:

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.