Machine learning (artificial intelligence) is widely regarded as a major technological revolution, presenting both challenges and opportunities for solving previously intractable problems. Climate change is universally recognized as the primary challenge faced by our generation, with nearly every aspect of human life being impacted by current and future climate changes. Climate change is even considered a major threat to future human health, affecting issues such as clean water access, heat wave exposure, and the spread of insect-borne diseases.
In our research group, we want to revolutionize the study of the relationship between climate and health. Through collaborative efforts among researchers with expertise in theoretical machine learning, information systems, action research, and data management, we aim to identify key research questions regarding the interactions between climate and health. We will then proceed to determine the necessary data to be collected and develop advanced machine learning methodologies for integrating climate and health data in predictive and causal modeling.
It is a daunting challenge that will require the participation of data engineers, software engineers, PhD students, postdoctoral researchers, professors, and master's students. We offer students a range of tasks related to this challenge, encompassing theoretical machine learning, scientific programming, and research on information systems. Some tasks will focus on data and questions closely related to applied research settings, while others will contribute more generally to theoretical advancements based on tailored synthetic datasets designed to address specific challenges in machine learning modeling within the climate and health field.
We are happy to meet and discuss how we can define a task that best aligns with your skills and interests.