The New York Stem Cell Foundation (NYSCF) Research Institute is a highly successful nonprofit whose mission is to accelerate cures through stem cell research.
Over the past decade, we’ve built automated infrastructure to perform cell biology at an unprecedented scale, allowing us to create some of the largest imaging-based datasets in the world. Using cell models from hundreds to thousands of donors, we are aiming to understand disease progression, unlock novel cellular phenotypes, and work to identify the patients who can benefit most from both existing and new therapeutics.
NYSCF is seeking an Associate Staff Scientist to join a growing team of data scientists and engineers in developing and deploying state-of-the-art image processing and analysis pipelines to extract disease-relevant phenotypic insights from high-content imaging experiments. In this role, you will analyze datasets of primary and stem cell-derived cell types, including neurons, beta cells, and fibroblasts.
You are a self-motivated applicant with an advanced degree in computer sciences, bioinformatics, medical imaging, physics, biomedical engineering, or a related quantitative discipline. You have a strong background in microscopic image acquisition and/or image processing and analysis and experience in analyzing the effects of genetic or pharmacological perturbations. You will have worked with tools such as CellProfiler and computer vision libraries, including OpenCV. You have applied machine/deep learning approaches to derive insights from image-derived features.
What you’ll do:
- Apply Microscopic Imaging Expertise: Apply advanced knowledge of microscopic imaging techniques to analyze and quality control imaging datasets.
- Lead Computational Analysis: Design, implement, and optimize computational workflows for analyzing large-scale high-content imaging screens.
- Advise on experimental design: Help define experimental conditions for high-content screens, identifying and accounting for potential confounders.
- Pipeline Development: Develop and maintain robust, scalable pipelines for data preprocessing, quality control, feature extraction, and downstream analysis.
- Innovate in Computer Vision: Employ state-of-the-art computer vision methods to extract meaningful biological insights from imaging data.
- Collaborative Research: Collaborate with experimental and computational scientists to integrate imaging data with other data modalities (e.g., genomics, transcriptomics).You will be a key team member involved in projects from inception to results.
- Programming and Software Development: Write efficient, well-documented Python code for image processing, machine learning, and statistical analysis.
- Data Interpretation and Visualization: Generate compelling visualizations and statistical summaries to communicate findings to a multidisciplinary team.
- Communication: Present your results at conferences and write scientific papers.
What we're looking for:
- PhD degree in a quantitative discipline (e.g., statistics, computational biology, biomedical engineering, computer science, applied mathematics, or similar) or equivalent practical experience.
- You are 0-5 years post-completion of your PhD, where you used Python to extract and clean imaging datasets before performing exploratory and statistical data analysis (using linear models, multivariate analysis, predictive modeling and stochastic approaches).
- You have experience working with biological datasets using tools such as CellProfiler.
- Strong computer vision expertise, and proficiency in modern image processing and machine learning frameworks (e.g., OpenCV, Scikit-learn, TensorFlow) are a plus.
- Familiarity with software engineering practices and experience developing production.
- Software and using cloud computing (AWS preferred).
- Familiarity with microscopes, scientific imaging, and image analysis.
- Demonstrated willingness to teach others and learn new techniques.
- Excellent analytical and communication skills.
Nice-to-Have Skills:
- Familiarity with scientific image analysis tools (ImageJ, FIJI).
- Familiarity with cell biology.