Research Technology Specialist - Human Nutrition Research Center on Aging

Overview

The Jean Mayer USDA Human Nutrition Research Center on Aging (HNRCA) at Tufts University is an internationally recognized leader in the study of nutrition and its role in healthy aging. Our multidisciplinary research spans molecular biology, clinical trials, epidemiology, behavioral science, and data science. The HNRCA is committed to developing precision nutrition strategies that optimize health span and functional independence in diverse aging populations. Collaborating across academic, governmental, and industry sectors, the Center fosters innovation and translational impact through state-of-the-art research infrastructure and scientific expertise.

What You'll Do

This is a grant funded position and is not eligible for severance pay.

The Research Technology Specialist will provide technical, computational, and analytical expertise to support research at the intersection of precision nutrition, healthy aging, artificial intelligence, wearable technologies, and biomedical data science.

This position will contribute to data-rich research projects involving human studies, observational cohorts, lifestyle interventions, biomarker analytics, wearable and sensor-derived data, and high-dimensional omics datasets. The successful candidate will work closely with HNRCA scientists, trainees, and other collaborators to develop, implement, and refine computational tools that transform complex biological, behavioral, and environmental data into actionable scientific insights.

The role is intended for an individual who can bridge biomedical research and advanced analytics, contributing both hands-on technical implementation and scientific interpretation. The position will support federally funded and collaborative research initiatives focused on precision nutrition, aging, cardiometabolic health, functional resilience, and individualized responses to diet and lifestyle interventions

• Design, implement, and maintain reproducible data pipelines for research studies in nutrition, aging, and health span.
• Support the integration, cleaning, harmonization, and analysis of complex datasets, including clinical, dietary, behavioral, wearable, sensor, imaging, metabolomic, microbiome, genetic, epigenetic, and other biomarker data.
• Develop and apply computational, statistical, and machine learning approaches to identify patterns of individual variability in response to diet, lifestyle, and environmental exposures.
• Build, test, and deploy prototype tools for data collection, monitoring, signal processing, visualization, and decision support in human research studies. Help establish best practices for data documentation, reproducibility, code management, data security, and compliance with human-subject research requirements.
• Analyze physiological, behavioral, spatial, or time-series data generated from wearable devices, mobile health tools, biosensors, GPS, accelerometers, continuous glucose monitors, or related platforms.
• Contribute to predictive modeling efforts aimed at identifying biological or behavioral subgroups, risk trajectories, and individualized intervention responses.
• Collaborate with faculty, trainees, statisticians, engineers, and biomedical researchers to ensure rigorous study design, appropriate data analysis, and meaningful interpretation of findings. Serve as a technical liaison between computational teams and biomedical investigators, helping translate research questions into analytical workflows and interpretable results.
• Contribute to scientific manuscripts, conference abstracts, grant applications, progress reports, and presentations. Generate high-quality technical reports, dashboards, visualizations, and summaries to support publications, grant proposals, internal decision-making, and external presentations.

What We're Looking For

Basic Requirements:

Knowledge and experience typically acquired by:

• Bachelor's Degree in biomedical engineering, computer science, data science, electrical engineering, bioinformatics, biostatistics, computational biology, applied mathematics, or a related quantitative or technical field.
• 3 years of relevant experience in data analytics, computational research support, biomedical data science, digital health, human performance research, nutrition, aging, neuroscience, public health, or related fields.
• Proficiency in Python and commonly used scientific computing and data science libraries, such as Pandas, NumPy, SciPy, scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn, or related tools.
• Experience working with complex, messy, or high-dimensional research datasets.
• Familiarity with statistical modeling, machine learning, signal processing, or time-series analysis.
• Ability to communicate technical findings clearly to both computational and non-computational scientific audiences.
• Strong organizational skills and ability to work collaboratively across multidisciplinary teams.

Preferred Qualifications:

• Master's Degree in biomedical engineering, computer science, data science, bioinformatics, biostatistics, computational biology, quantitative public health, nutrition science, or a related field.
• Experience analyzing wearable sensor data, mobile health data, accelerometry, continuous glucose monitoring, physiological signals, GPS/location data, or other free-living behavioral data.
• Experience with cloud computing, Git/GitHub, reproducible workflows, database management, REDCap, SQL, R, or workflow management tools.
• Experience with multi-omics or biomarker datasets, including metabolomics, proteomics, microbiome, genomics, epigenomics, or lipidomics.
• Experience contributing to peer-reviewed manuscripts, grant proposals, technical reports, or scientific presentations.
• Familiarity with nutrition, aging biology, cardiometabolic health, frailty, resilience, cognitive aging, or health span research.
• Familiarity with AI/ML applications in precision nutrition, digital health, public health, gerontology, or personalized medicine.
• Ability to develop user-friendly visualizations, dashboards, or prototype research tools.
• Knowledge of data governance, privacy, security, and regulatory considerations relevant to human-subject biomedical research.