The Department of Engineering Mathematics at the University of Bristol is seeking an outstanding candidate to fill the role of Professor in Artificial Intelligence. You will have the opportunity to provide visionary leadership to the department and its staff, students, & partners, helping to strengthen and further develop our already impressive research and teaching programs in AI. Our Intelligent Systems Group supports the Faculty of Engineering's AI/Data Science Theme, fostering an inclusive environment for all.

The KINDI Center for Computing Research at the College of Engineering in Qatar University is seeking high-caliber candidates for a research faculty position at the level of assistant professor in the area of artificial intelligence (AI). The applicant should possess a Ph.D. degree in Computer Science or Computer Engineering or related fields from an internationally recognized university and should demonstrate an outstanding research record in AI and related subareas (e.g., machine/deep learning (ML/DL), computer vision, robotics, natural language processing, etc.) and fields (e.g., data science, big data analytics, etc.). Candidates with good hands-on experience are preferred. The position is available immediately.

Changes in ocular hemodynamics may be indicative of pathological conditions in the eye (e.g. glaucoma, age-related macular degeneration), but also elsewhere in the body (e.g. systemic hypertension, diabetes, neurodegenerative disorders). Thanks to its transparent fluids and structures that allow the light to go through, the eye offers a unique window on the circulation from large to small vessels, and from arteries to veins. Deciphering the causes that lead to changes in ocular hemodynamics in a specific individual could help prevent vision loss as well as aid in the diagnosis and management of diseases beyond the eye. In this talk, we will discuss how mathematical and computational modelling can help in this regard. We will focus on two main factors, namely blood pressure (BP), which drives the blood flow through the vessels, and intraocular pressure (IOP), which compresses the vessels and may impede the flow. Mechanism-driven models translates fundamental principles of physics and physiology into computable equations that allow for identification of cause-to-effect relationships among interplaying factors (e.g. BP, IOP, blood flow). While invaluable for causality, mechanism-driven models are often based on simplifying assumptions to make them tractable for analysis and simulation; however, this often brings into question their relevance beyond theoretical explorations. Data-driven models offer a natural remedy to address these short-comings. Data-driven methods may be supervised (based on labelled training data) or unsupervised (clustering and other data analytics) and they include models based on statistics, machine learning, deep learning and neural networks. Data-driven models naturally thrive on large datasets, making them scalable to a plethora of applications. While invaluable for scalability, data-driven models are often perceived as black- boxes, as their outcomes are difficult to explain in terms of fundamental principles of physics and physiology and this limits the delivery of actionable insights. The combination of mechanism-driven and data-driven models allows us to harness the advantages of both, as mechanism-driven models excel at interpretability but suffer from a lack of scalability, while data-driven models are excellent at scale but suffer in terms of generalizability and insights for hypothesis generation. This combined, integrative approach represents the pillar of the interdisciplinary approach to data science that will be discussed in this talk, with application to ocular hemodynamics and specific examples in glaucoma research.