Develop and apply state-of-the-art AI/ML, statistical, and computational frameworks to analyze genomics, transcriptomics, proteomics, metabolomics, single-cell, and multi-omics datasets.
Lead the design and execution of spatial omics analyses at massive scale, integrating imaging-based, sequencing-based, and multiplexed spatial platforms to uncover tissue architecture, cellular neighborhoods, and microenvironmental dynamics.
Build scalable pipelines to preprocess, QC, harmonize, and integrate terabyte- to petabyte-scale spatial omics datasets, enabling discovery-ready data layers and advanced modeling.
Deploy, adapt and develop agent-based models (ABM) to simulate cellular interactions, tissue-level organization, and dynamic biological processes, incorporating outputs from multimodal omics and spatial measurements.
Fuse mechanistic models with ML/AI frameworks to generate hybrid predictive systems for target discovery, perturbation response, and disease progression modeling.
Deploy and create novel ML architectures, including deep learning, generative models, graph neural networks, and causal inference frameworks that are tailored for biological complexity.
Design and implement scalable algorithms for high-dimensional, multimodal integration of spatial, molecular, and phenotypic data.
Map, influence, and guide the development of computational and data architecture needed to support next-generation omics and ML workloads.

Requirements

Minimum of a Ph.D. in Computational Biology, Bioinformatics, Computer Science, Statistical Genetics, Systems Biology, Applied Mathematics/Physics, or a related quantitative discipline.
Minimum of 9 years of post-doctoral, industry or academic experience applying advanced computational, statistical, and machine-learning methods to biological problems.
Deep expertise across multiple omics modalities, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics (e.g., spatial transcriptomics, multiplexed imaging, spatial proteomics).
Demonstrated ability to analyze, integrate, and interpret very large-scale, multimodal datasets (multi-TB to PB scale), including the design of scalable pipelines and distributed computation strategies.
Expert-level proficiency in modern ML/AI frameworks, such as PyTorch, TensorFlow, JAX, scikit-learn, and deep-learning architectures relevant to biological modeling.
Strong background in agent-based modeling, systems biology modeling, or hybrid mechanistic-ML modeling frameworks.
Proven ability to design and influence data and computational architectures, including experience working with cloud-native analytical ecosystems (Azure, AWS, or GCP) and large-scale data engineering workflows.
Demonstrated scientific leadership as an individual contributor, including the ability to independently drive complex research programs, set technical direction, and influence cross-functional strategy.
A strong publication record in high-impact journals or top-tier ML/AI conferences, reflecting innovation in computational biology or applied machine learning.
Proficiency in Python and experience with scientific computing libraries (NumPy, SciPy, pandas) and workflow orchestration tools.

Benefits

medical insurance
dental insurance
vision insurance
life insurance
short and long-term disability
business accident insurance
group legal insurance
retirement plan (pension)
savings plan (401(k))
annual performance bonus
parental leave
vacation time
sick time
holiday pay
volunteer leave
caregiver leave
military spouse time-off

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI/ML frameworkscomputational frameworksstatistical methodsagent-based modelingdeep learninggenerative modelsgraph neural networkscausal inference frameworkshigh-dimensional data integrationdata architecture design

Soft Skills

scientific leadershipindependent researchtechnical directioncross-functional influence