Data Scientist Fellow, Bioinformatics and Image Analysis

Position Overview

The Postdoctoral Fellow will turn the data produced by the autonomous laboratory in Bethesda into results that the lab can act on. The platform runs biological experiments on the BAB400 robot, which comes with a Cytation 9 imaging system, and the main data stream is microscopy images. The Fellow will build and run the image analysis and bioinformatics pipelines that measure what happened in each experiment, combine those measurements with environmental monitoring data collected alongside the experiments, and return quantitative readouts to the autonomous loop so that it can decide what to run next.

The Fellow will work side by side with the Laboratory Automation Engineer and with the biologists and data engineers of IDSS. This is a hybrid research position: the Fellow is expected to contribute new methods, publish, and present, in addition to keeping the analysis running reliably.

Key Responsibilities

Image analysis pipelines. Develop and maintain automated pipelines for Cytation 9 images, including quality control, illumination and background correction, segmentation, tracking, and feature extraction for cultures such as iPSC colonies and, later, organoids.

Quantitative readouts for the loop. Define and validate metrics that describe culture state and growth (for example confluence, colony morphology, differentiation markers, and viability), with uncertainty estimates, so that they can be used as decision inputs by the autonomous system.

Closing the loop. Work with the Laboratory Automation Engineer to deliver analysis results back to the workflow orchestration layer in a machine-readable form and on a timescale the loop can use. Define the interfaces, the failure behavior, and the checks that stop bad data from driving a bad decision.

Environmental data integration. Collect, clean, and align environmental monitoring data (for example temperature, CO2, O2, humidity, and other sensor streams) with imaging results. Analyze how conditions relate to culture outcomes and flag drift, excursions, and sensor faults.

Statistical and machine learning methods. Apply and develop statistical, machine learning, and deep learning methods for image and multimodal data, with attention to validation, reproducibility, and batch effects. Use the existing Cytation and instrument software where it helps, and build custom methods where it does not.

Bioinformatics. Integrate downstream omics data, including single-cell data, generated from the experiments where they are available, and link them to image-derived phenotypes.

Data management. Work with IDSS data engineers so that images, derived measurements, and metadata are stored, versioned, and findable in line with FAIR principles and NIH data management and sharing requirements.

Research and communication. Publish methods and results, present to NIAID and collaborators, document pipelines and standard operating procedures, and train staff to use the analysis tools.

Required Qualifications

Education. 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.

Image analysis. Demonstrated experience analyzing microscopy or other biological image data, including segmentation and quantitative feature extraction.

Programming. Strong Python, including scientific and imaging libraries (NumPy, SciPy, pandas, scikit-image, or similar) and at least one machine learning framework (PyTorch, TensorFlow, or similar).

Statistics and experimental design. Strong grounding in statistics and experimental design, and experience validating analysis methods.

Reproducible practice. Experience with Git and reproducible analysis practices, and with working in a Linux or high-performance computing environment.

Research record. A record of research output, such as peer-reviewed publications, preprints, or released software.

Collaboration. Ability to work in a close, iterative partnership with an automation engineer and biologists, and to explain methods and results clearly to both.

Clearance. Ability to obtain and maintain NIH/HHS security clearance and meet federal contractor suitability requirements.

Preferred Qualifications

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 with 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 with 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).

Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.

This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location.

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Salary Range

$90,000—$100,000 USD