Applied Researcher in Data-driven Nutrition
Core
Developing predictive dynamical models and digital decision-support tools for nutrition and health using large-scale diet, health, and omics data.
Role type
Applied Researcher (Data-driven Nutrition & Health)
Builds
Predictive models, digital decision-support tools, and analysis of time series/dynamic processes for disease prevention.
Domain
Biomedicine, Nutrition, Epidemiology, Food Science
Deliverable
production ML models | dashboards & analysis
Required skills
Statistical modelling, AI & machine learning, R, Python, time series analysis, causal inference, omics data analysis, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI
Preferred skills
Experience with cohort/registry/clinical data, academic-industrial project management, supervising PhD students/postdocs
Technologies
R, Python, CGM sensors, activity trackers, wearable sensors
Responsibilities
Lead research projects in data-driven nutrition; analyze time series data and dynamic processes; develop predictive dynamical models; serve as a bridge between domain researchers and quantitative experts; supervise PhD students and postdocs.
Seniority
Senior, hands-on IC with research leadership
