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Joshua Granek

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Postdoctoral Associate in Precision Microbiome Engineering and Machine Learning Duke University in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Nov 30, 2026

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Country

United States

University

Duke University

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Keywords

Computer Science
Environmental Science
Biomedical Engineering
Deep Learning
Biology
Computational Biology
Statistics
Bacterial Genomic
Bioinformatic
ML
bio engineering

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About this position

PreMiEr at Duke University is offering a postdoctoral research opportunity in precision microbiome engineering. The project sits at the intersection of artificial intelligence, microbial genomics, and microbiome science, with a strong emphasis on computational method development for complex microbial communities.

The postdoctoral associate will work on large-scale DNA sequencing data to identify health-relevant patterns in built-environment microbiomes. The research includes developing and applying DNA language model approaches, designing machine learning methods for prediction and classification from metagenomic sequence data, and comparing representation-learning strategies with conventional bioinformatics workflows. A further goal is to evaluate how well these approaches generalize across independent studies, sequencing platforms, and environmental settings.

The role is embedded in PreMiEr and connected to broader efforts in microbiome monitoring, risk assessment, and precision microbiome engineering. The selected researcher will collaborate with biologists, engineers, microbiologists, and data scientists, contributing to reproducible computational workflows, benchmarking analyses, and collaborative interdisciplinary projects. There may also be opportunities to mentor graduate and undergraduate researchers and to participate in educational and training activities within the Duke research community.

The position is supervised by Dr. Joshua Granek and Dr. Pixu Shi in the Duke University School of Medicine.

Funding details

Available

What's required

Ph.D. or equivalent doctoral degree in Bioinformatics, Computational Biology, Environmental Engineering, Biostatistics, Computer Science, Genomics, Data Science, or a related quantitative field. Applicants should have demonstrated experience conducting independent computational or data-intensive research, strong programming skills in R and/or Python, experience analyzing high-throughput sequencing or other large biological datasets, evidence of scholarly productivity such as peer-reviewed publications, preprints, conference presentations, or open-source software contributions, strong written and oral communication skills, and the ability to work effectively in interdisciplinary research teams. Preferred qualifications include metagenomics, microbiome research, microbial genomics, machine learning, deep learning, representation learning, high-performance computing, reproducible workflow systems such as Snakemake, Nextflow, or Apptainer, long-read sequencing technologies such as Oxford Nanopore or PacBio, and experience developing reproducible computational workflows and research software.

How to apply

Apply through the Academic Jobs Online portal using the provided application link. Submit a cover letter, curriculum vitae, and the names and email addresses of three references. Review begins immediately and continues until the position is filled.

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Professors

Assistant Professor at Duke University

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Research Interests

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Immunology

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Metabolic Biology

Biology

Assistant Professor at Duke University

University Name
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United States

Research Interests

Statistics

Bioinformatic

Bacterial Genomic

Deep Learning

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