Data Scientist Fellow, Bioinformatics and Image Analysis

Axle • Bethesda, MD

2d • Hybrid

About The Position

Axle is seeking a Postdoctoral Fellow to analyze data produced by an autonomous laboratory in Bethesda. The laboratory runs biological experiments using a BAB400 robot with a Cytation 9 imaging system, producing microscopy images as its primary data stream. The Fellow will build and operate image-analysis and bioinformatics pipelines to measure experimental outcomes, integrate those measurements with environmental monitoring data, and provide quantitative readouts to the autonomous system so it can determine subsequent experiments.

The Fellow will work closely with the Laboratory Automation Engineer and IDSS biologists and data engineers. This hybrid research position combines developing new methods, publishing and presenting research, and ensuring analysis pipelines operate reliably. The role includes image processing, quantitative measurement and uncertainty estimation, delivery of results to workflow orchestration systems, integration of environmental and omics data, and data management consistent with FAIR principles and NIH requirements.

The position requires a doctoral degree in a relevant quantitative field, normally completed within the last five years; demonstrated biological image-analysis experience; strong Python, statistics, and experimental-design skills; and reproducible research practices. The candidate must be able to obtain and maintain NIH/HHS security clearance and meet federal contractor suitability requirements. The anticipated base compensation range is $90,000–$100,000.

Requirements

• Doctoral degree (PhD or equivalent) in bioinformatics, computational biology, biomedical engineering, computer science, physics, or a related quantitative field, normally completed within the last five years.
• Demonstrated experience analyzing microscopy or other biological image data, including segmentation and quantitative feature extraction.
• Strong Python skills, including scientific and imaging libraries such as NumPy, SciPy, pandas, and scikit-image or similar.
• Experience with at least one machine-learning framework, such as PyTorch or TensorFlow.
• Strong grounding in statistics and experimental design, and experience validating analysis methods.
• Experience with Git and reproducible analysis practices, and with Linux or high-performance computing environments.
• Research output such as peer-reviewed publications, preprints, or released software.
• Ability to work in a close, iterative partnership with an automation engineer and biologists, and to explain methods and results clearly to both.
• Ability to obtain and maintain NIH/HHS security clearance and meet federal contractor suitability requirements.

Nice To Haves

• Experience with high-content or live-cell imaging systems, especially Agilent BioTek Cytation or Gen5 software.
• Experience with image-analysis tools such as CellProfiler, Cellpose, StarDist, napari, or ImageJ/Fiji, and deep-learning methods for segmentation and classification.
• Experience with stem cell, iPSC, organoid, or tissue-culture systems and their imaging.
• Experience with time-series or multimodal analysis, including sensor and environmental monitoring data.
• Experience with self-driving laboratories, active learning, or Bayesian optimization.
• Experience with single-cell omics analysis (scRNA-seq) and workflow tools such as Nextflow or Snakemake.
• Familiarity with laboratory orchestration software such as MADSci, and with Docker or Kubernetes.
• Experience with high-performance computing environments, for example NIH Biowulf.

Responsibilities

• Develop and maintain automated Cytation 9 image-analysis pipelines, including quality control, illumination and background correction, segmentation, tracking, and feature extraction for cultures such as iPSC colonies and organoids.
• Define and validate quantitative metrics of culture state and growth, such as confluence, colony morphology, differentiation markers, and viability, including uncertainty estimates for autonomous-system decisions.
• Work with the Laboratory Automation Engineer to deliver analysis results to the workflow orchestration layer in a machine-readable format and on a usable timescale; define interfaces, failure behavior, and safeguards against decisions based on bad data.
• Collect, clean, and align environmental monitoring data, such as temperature, CO2, O2, and humidity, with imaging results; analyze relationships with culture outcomes and flag drift, excursions, and sensor faults.
• Apply and develop statistical, machine-learning, and deep-learning methods for image and multimodal data, with attention to validation, reproducibility, and batch effects.
• Use existing Cytation and instrument software where appropriate and build custom methods when needed.
• Integrate available downstream omics data, including single-cell data, and link it to image-derived phenotypes.
• Work with IDSS data engineers to ensure images, derived measurements, and metadata are stored, versioned, and findable in accordance with FAIR principles and NIH data-management and sharing requirements.
• Publish methods and results, present to NIAID and collaborators, document pipelines and standard operating procedures, and train staff to use the analysis tools.
• Collaborate closely and iteratively with the Laboratory Automation Engineer, biologists, and data engineers, and communicate methods and results clearly.

Benefits

• 100% medical, dental, and vision coverage for employees
• Paid time off and paid holidays
• 401(k) match up to 5%
• Educational benefits for career growth
• Employee referral bonus
• Flexible spending accounts for healthcare (FSA), dependent care (DCAP), and transportation (TRN)
• Parking reimbursement account (PRK)

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree