Research Support Specialist

Stony Brook University•Stony Brook, NY

8d•Onsite

About The Position

A Research Support Specialist position is available in the laboratory of Dr. Prerana Shrestha, Department of Neurobiology and Behavior, Stony Brook University. Research in the Shrestha laboratory focuses on understanding the translational control of memory processes using advanced in vivo imaging, molecular biology, and behavioral paradigms in rodent models. We are seeking a motivated Data Engineer and Data Scientist to own the computational infrastructure of the laboratory. The successful candidate will design, build, and maintain modular Python pipelines for fiber photometry signal processing and behavioral video analysis; run and monitor HPC workloads on SeaWulf; produce publication-quality visualizations; and collaborate closely with PhD students, postdocs, and the PI to ensure analytical rigor and reproducibility across all ongoing projects. The ideal candidate brings a combination of strong engineering discipline (clean, documented, version-controlled code), quantitative depth (signal processing, statistical modeling, ML), and scientific curiosity. The successful incumbent will possess excellent communication skills and attention to detail are essential, as outputs directly support manuscript preparation and grant applications.

Requirements

Bachelor's Degree (foreign equivalent or higher) in Data Science, Computer Science, Statistics, Computational Biology, or a closely related quantitative field.
Experience in Python for scientific computing, including NumPy, SciPy, Pandas, and Scikit-learn.
Demonstrated experience designing, implementing, and maintaining automated data pipelines for large-scale time-series or imaging datasets.
Experience with HPC/cluster computing environments, including SLURM job scheduling and GPU-accelerated processing.
Experience with applied statistics, signal processing, and machine learning.

Nice To Haves

Master’s Degree (foreign equivalent or higher) in Data Science with graduate-level coursework in statistical learning, computer vision, big data systems, NLP, or data visualization.
Hands-on experience processing in vivo fluorescence imaging data: z-score normalization, PETH construction, Savitzky-Golay filtering, baseline windowing, and time-warping methods (e.g., piecewise linear interpolation to median CS duration).
Experience with animal pose estimation tools (DeepLabCut or equivalent) for automated behavioral quantification from video.
Proficiency in automated report and visualization generation: publication-quality figures (Matplotlib, Seaborn, Plotly, D3.js), Excel/PowerPoint automation, and multi-panel dashboards.
Experience with cloud platforms (AWS, GCP) and containerized deployments (Docker, Kubernetes).
Prior experience in a neuroscience, biology, or translational research laboratory environment.
Experience contributing to manuscript methods sections or scientific reports.

Responsibilities

Design, implement, and maintain modular Python data pipelines for fiber photometry calcium imaging (GCaMP8m): Savitzky-Golay signal smoothing, z-score normalization, PETH construction with pre-event baseline windowing, piecewise linear time-warping to global median CS duration, AUC/Ymax feature extraction, and cohort-level statistical summaries exported to Excel and figures.
Run GPU-accelerated DeepLabCut inference (SLURM/V100 nodes) on behavioral video data; extract freeze duration, shuttling, object exploration, and locomotion metrics for Open Field, Novel Object Recognition, and Signaled Active Avoidance paradigms; produce integrated neural-behavioral overlays on a common time axis.
Generate publication-quality multi-panel figures (PETHs, heatmaps, raster plots, behavior overlays, summary statistics) using Matplotlib/Seaborn/Plotly; automate Excel and PowerPoint outputs for collaborator review and grant submissions; contribute to methods sections of manuscripts.
Manage SLURM job submissions on SeaWulf HPC; maintain conda environments and package dependencies; version-control all analysis code on GitHub with documented APIs and parameter configurations enabling reproducible re-analysis.
Other Duties as Assigned