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Microbiome Machine Learning Jobs (NOW HIRING)

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$72K - $80K/yr

... microbiome are also of interest. This program is funded through multiple R01 studies and are part ... Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in ...

... microbiome efforts. * Perform analyses in a transparent manner and to clearly communicate analysis ... Experience in machine learning and biostatistics. * Experience in pathway analysis. * Experience ...

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$72K - $80K/yr

... microbiome are also of interest. This program is funded through multiple R01 studies and are part ... Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in ...

... microbiome efforts. * Perform analyses in a transparent manner and to clearly communicate analysis ... Experience in machine learning and biostatistics. * Experience in pathway analysis. * Experience ...

... microbiome efforts. * Perform analyses in a transparent manner and to clearly communicate analysis ... Experience in machine learning and biostatistics. * Experience in pathway analysis. * Experience ...

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Microbiome Machine Learning information

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How much do microbiome machine learning jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for microbiome machine learning in the United States is $21.33, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $22.84 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Microbiome Machine Learning Specialist, and why are they important?

To thrive as a Microbiome Machine Learning Specialist, you need a strong background in bioinformatics, microbiology, statistics, and proficiency in programming languages such as Python or R, typically supported by an advanced degree in a related field. Familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow), data visualization tools, and experience with microbiome analysis pipelines are crucial. Strong problem-solving abilities, attention to detail, and effective interdisciplinary communication skills help you collaborate with researchers and translate complex data into actionable insights. These skills are essential for accurately analyzing large-scale biological datasets and contributing to advancements in microbiome research and applications.

How do professionals in microbiome machine learning typically collaborate with biologists and clinicians during research projects?

Professionals in microbiome machine learning often work closely with biologists and clinicians by translating complex biological questions into computational models and interpreting the results in a meaningful biological context. Regular meetings and interdisciplinary workshops are common, ensuring that machine learning practitioners understand the experimental design, data sources, and clinical relevance. Successful collaboration requires clear communication, as well as a willingness to iterate on analytical approaches based on feedback from domain experts. This teamwork leads to more robust discoveries and the practical application of machine learning insights in microbiome research.

What is a Microbiome Machine Learning specialist?

A Microbiome Machine Learning specialist is a professional who applies machine learning and data science techniques to analyze and interpret complex datasets related to microbial communities (microbiomes). These specialists work at the intersection of computational biology, microbiology, and artificial intelligence to uncover patterns, predict outcomes, and derive insights from microbiome data. Their work often contributes to advancements in healthcare, agriculture, and environmental science by helping to understand how microbial populations affect health and ecosystems.

What is the difference between Microbiome Machine Learning vs Microbiome Data Analyst?

AspectMicrobiome Machine LearningMicrobiome Data Analyst
Required CredentialsBackground in machine learning, data science, biologyBackground in biology, data analysis, statistics
Work EnvironmentResearch labs, biotech companies, academiaResearch institutions, healthcare, biotech firms
Industry UsageDeveloping predictive models, algorithms for microbiome dataAnalyzing microbiome datasets, reporting findings

Microbiome Machine Learning specialists focus on developing algorithms and models to interpret microbiome data, often requiring advanced programming and machine learning skills. In contrast, Microbiome Data Analysts primarily interpret and visualize microbiome datasets, emphasizing statistical analysis. Both roles are vital in microbiome research but differ in technical depth and focus areas.

Infographic showing various Microbiome Machine Learning job openings in the United States as of May 2026, with employment types broken down into 75% Full Time, 24% Part Time, and 1% Contract. Highlights an 97% Physical, and 3% Remote job distribution, with an average salary of $44,363 per year, or $21.3 per hour.
Postdoctoral Fellow-MSH

Postdoctoral Fellow-MSH

Mount Sinai Health System

Manhattan, NY • On-site

$53K - $73K/yr

Full-time

Posted 22 days ago


Mount Sinai rating

7.8

Company rating: 7.8 out of 10

Based on 280 frontline employees who took The Breakroom Quiz

131st of 865 rated healthcare providers


Job description

Roles & Responsibilities:

This PostDoc position is available with Dr. Rosalind Wright's research group at the Icahn School of Medicine at Mount Sinai in New York City (NYC). This is an exciting opportunity to join a mature transdisciplinary team science environment embedded in a vibrant exposomic ecosystem in our Institute for Exposomic Research. Funding is guaranteed for 2 years with potential for further support. Dr. Wright's research program leverages ongoing pregnancy cohort studies investigating the impact of prenatal and early childhood exposures (both chemical and non-chemical) on child developmental outcomes including growth and obesity, respiratory disorders including asthma and lung development, neurocognition and behavioral development, emerging psychological dysfunction including internalizing/ externalizing problems and sleep disorders in preschool and early school-aged children. Chemical exposures include toxic metals and their mixtures, ambient air pollution and their mixtures, and organic chemicals with an extensive biorepository to facilitate expanded exposure assessment.Consideration of modifying effects of nutrition is a major developing interest among the group. We have substantial infrastructure facilitating geocoding and linkage with administrative databases to consider community factors such as crime/violence, access to healthy foods, green space, etc. A particular focus is on developing a platform to allow multi-omic approaches to interrogating these complex associations and their biologic underpinnings including available data on extracellular vesicle-related microRNAs and long noncoding RNAs, as well as proteomic, mitochondriomic, metablomic, epigenomic dataMetabolomics and microbiome are also of interest. This program is funded through multiple R01 studies and are part of the NIH funded national ECHO (Environmental Influences on Children's Health Outcomes) program. Other notable support includes a NIEHS-funded K12 program, NCATS funded KL2/Tl1 training programs, and a NIEHS P30 Center Grant all of which provide opportunities for pilot funding available to postdocs throughout training. These programs provide significant support for the postdoctoral candidates to submit a NIH funded Career Development Award or other grant mechanism to facilitate transition to independence. The successful postdoc will lead epidemiology and computational investigations incorporating exposomic approaches leveraging this reach ecosystem.
Examples of active projects include:
1. Prenatal metal-stress mixtures and transdiagnostic pathways to preadolescent internalizing disorders: Role of placental molecular signaling
2. Prenatal metal mixtures and neurodevelopment: Role of placental extracellular microRNAs
3. ECHO consortium on Perinatal Programming of Neurodevelopment
4. Maternal traumatic stress, oxidative stress, antioxidant exposures, and child asthma and lung function
5. Prenatal metals-stress mixtures and sleep disruption in preschoolers 

Requirements:

Applicants should possess a Ph.D. in Environmental and/or Molecular Epidemiology, Social Epidemiology, Nutritional Epidemiology, Developmental Psychology, or in other relevant disciplines, such as Statistics/Biostatistics, Computational Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in environmental health. Strong quantitative skills relevant to this broad program such as practical experience working with complex epidemiology data, familiarity with the R statistical software packages and excellent oral communication and scientific writing are desirable. Experience with high dimensional molecular data such as metabolomics, mitochondriomics, Illumina BeadChip methylation (450K, 850K or EPIC) and RNA-seq data is a strength.
Interested individuals should send a cover letter, curriculum vitae, two sample publications, and the names/phone numbers of three people who could provide letters of reference by email to Ms. Suzy Allen, Administrative Manager: Suzy.Allen@mssm.edu.

Strength through Unity and Inclusion

The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai's unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual.

At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history.

About the Mount Sinai Health System:

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, more than 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time - discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients' medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high "Honor Roll" status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Report's "Best Children's Hospitals" ranks Mount Sinai Kravis Children's Hospital among the country's best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek's "The World's Best Smart Hospitals" ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.

Equal Opportunity Employer

The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization.

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Applicants should possess a Ph.D. in Environmental and/or Molecular Epidemiology, Social Epidemiology, Nutritional Epidemiology, Developmental Psychology, or in other relevant disciplines, such as Statistics/Biostatistics, Computational Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in environmental health. Strong quantitative skills relevant to this broad program such as practical experience working with complex epidemiology data, familiarity with the R statistical software packages and excellent oral communication and scientific writing are desirable. Experience with high dimensional molecular data such as metabolomics, mitochondriomics, Illumina BeadChip methylation (450K, 850K or EPIC) and RNA-seq data is a strength.
Interested individuals should send a cover letter, curriculum vitae, two sample publications, and the names/phone numbers of three people who could provide letters of reference by email to Ms. Suzy Allen, Administrative Manager: Suzy.Allen@mssm.edu.

Compensation Statement

The Mount Sinai Health System (MSHS) provides salary ranges that comply with the New York City Law on Salary Transparency in Job Advertisements. The salary range for this role is $72,500.00 - $80,000.00 Annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.

Non-Bargaining Unit, ICC - Pediatrics Pulmonology Research - ISM, Icahn School of Medicine

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