Research Associate in Deep Generative Modelling for Infectious Diseases
Core
Developing principled, scalable deep generative models and neural surrogate tools to address computational challenges in fitting complex disease models to data, specifically for antimalarial drug resistance in sub-Saharan Africa.
Role type
Research Associate in Deep Generative Modelling
Builds
Scalable deep generative models and neural surrogate models for infectious disease simulation
Domain
Global health, infectious disease modelling, deep learning
Deliverable
production ML models
Required skills
Deep generative modelling, simulation-based inference, neural approaches to spatial/spatiotemporal Bayesian inference, Python programming, PyTorch or JAX proficiency, probabilistic machine learning
Preferred skills
Methodological innovation, cross-disciplinary communication
Technologies
PyTorch, JAX, Python
Responsibilities
Drive methodological research in deep generative modelling and simulation-based inference; develop tools for fitting complex disease models to data; validate methods against real scientific problems
Seniority
PhD level researcher
