Research Associate in Machine Learning and Computational Psychiatry for Digital Mental Health Interventions
Core
Developing AI-guided smartphone-based mental health interventions for adolescents by building mathematically grounded, personalised systems that infer mental state from multimodal behavioural data and deliver adaptive interventions.
Role type
Postdoctoral Research Associate (Machine Learning & Computational Psychiatry)
Builds
Personalised digital twin models, reinforcement learning/sequential decision-making models for adaptive intervention delivery, and multimodal models of adolescent mental state.
Domain
Digital mental health, computational psychiatry, adolescent health
Deliverable
production ML models
Required skills
probabilistic modelling, time-series modelling, latent-variable models, state-space models, reinforcement learning, sequential decision-making, representation learning, Bayesian methods, Python, deep learning frameworks (PyTorch, JAX, TensorFlow)
Preferred skills
experience with healthcare, mental health, or mobile sensing data
Technologies
PyTorch, JAX, TensorFlow
Responsibilities
Developing multimodal models of adolescent mental state from longitudinal mobile and self-report data; Designing latent-state, state-space, probabilistic, or representation-learning approaches for modelling mental health trajectories; Building personalised digital twin models integrating behavioural, contextual, and questionnaire-derived information; Developing reinforcement learning, contextual bandit, or sequential decision-making models for adaptive intervention delivery; Tackling core challenges such as partial observability, uncertainty, missingness, delayed rewards, and non-stationarity; Contributing to the prospective deployment and evaluation of AI-driven interventions within a real-world school-based study
Seniority
Postdoctoral, hands-on IC
