POST-DOCTORAL RESEARCH ASSOCIATE IN BAYESIAN MACHINE LEARNING FOR CLIMATE AND HEALTH DATA

We are seeking a Post-doctoral Research Associate in Bayesian Machine Learning, with a focus on modelling complex spatial and temporal climate data, for a fixed term of 3 years, starting on 1st October or as mutually agreed.
Position Overview: The successful candidate will join the Faculty of Mathematics and Data Science at Emirates Aviation University in Dubai, contributing to cross-disciplinary projects by developing novel multivariate statistical and computational methods tailored to complex climate change and health-related data. Our research approach is centred on probabilistic Bayesian machine learning models and methods that integrate heterogeneous, complementary climate, environmental, and health data.

QUALIFICATIONS:

• Ph.D. in statistics, mathematics, data science, or a related field, with a strong foundation in Bayesian statistics and probabilistic machine learning methods.
• Experience with advanced Bayesian models, Gaussian process models, mathematical modelling, and multivariate statistical methods.
• Strong computational skills, including experience with Python programming.
• A collaborative mindset and the ability to work across disciplines.

• Model Development: Focus on advancing machine learning frameworks designed to assess the impact of climate change on public health, including phenomena such as heatwaves and flooding. This role involves leveraging cutting-edge approaches like Gaussian processes, physics-informed neural networks, and Bayesian networks. These models are critical for understanding the intricate relationships between climate variables and health outcomes. You will develop these models and computational inference algorithms to effectively analyse and predict the public health impacts of large-scale and complex climate data.
• Predictive Model Evaluation: Contribute to the development of innovative techniques for comparing and assessing predictive models, with a particular emphasis on validating and benchmarking model predictions within the context of climate change impacts on public health.
• Collaboration: Work closely with public health experts and other stakeholders to apply the developed machine learning methods to extensive datasets related to climate change impacts, such as heatwaves and flooding. Your role will include assessing and validating the proposed models and methods. While prior experience in public health is advantageous, it is not essential.