The PhD research focuses on the fairness, explainability, and robustness of machine learning systems within the framework of causal counterfactual analysis using formalisms from probabilistic graphical models, probabilistic circuits, and structural causal models.

The Intelligent and Interactive Systems lab uses machine learning to enhance the flexibility, robustness, generalization and explainability of robots and vision systems, focusing on methods for learning about structure, function, and other concepts that describe the world in actionable ways. Three University-Assistant Positions involve minor teaching duties with negotiable research topics within the lab's scope. One Project Position involves the integration of robotic perception and execution mechanisms for task-oriented object manipulation in everyday environments, with a focus on affordance-driven object part segmentation and object manipulation using reinforcement learning.

The PhD research focuses on the fairness, explainability, and robustness of machine learning systems within the framework of causal counterfactual analysis using formalisms from probabilistic graphical models, probabilistic circuits, and structural causal models.

A fully funded PhD position in Machine Learning and Computer Vision is available at Uppsala University, Sweden. The position is a part of the Beijer Laboratory for Artificial Intelligence Research, funded by Kjell and Märta Beijer Foundation. In this project you will join us in conducting fundamental machine learning research and developing principled foundations of vision-language models, with opportunities to validate the methods on challenging real-world problems involving computer vision.

For the recently established Cluster of Excellence CoE Bilateral Artificial Intelligence (BILAI), funded by the Austrian Science Fund (FWF), we are looking for more than 50 PhD students and 10 Post-Doc researchers (m/f/d) to join our team at one of the six leading research institutions across Austria. In BILAI, major Austrian players in Artificial Intelligence (AI) are teaming up to work towards Broad AI. As opposed to Narrow AI, which is characterized by task-specific skills, Broad AI seeks to address a wide array of problems, rather than being limited to a single task or domain. To develop its foundations, BILAI employs a Bilateral AI approach, effectively combining sub-symbolic AI (neural networks and machine learning) with symbolic AI (logic, knowledge representation, and reasoning) in various ways. Harnessing the full potential of both symbolic and sub-symbolic approaches can open new avenues for AI, enhancing its ability to solve novel problems, adapt to diverse environments, improve reasoning skills, and increase efficiency in computation and data use. These key features enable a broad range of applications for Broad AI, from drug development and medicine to planning and scheduling, autonomous traffic management, and recommendation systems. Prioritizing fairness, transparency, and explainability, the development of Broad AI is crucial for addressing ethical concerns and ensuring a positive impact on society. The research team is committed to cross-disciplinary work in order to provide theory and models for future AI and deployment to applications.

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.

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classification, and prognostic prediction of disease progression, both from images alone and accompanied by demographic and genetic information. The approaches discussed will include deep feature extraction, label transfer, and multi-modal, multi-task training. Cluster analysis, an unsupervised machine learning approach to data classification, will be demonstrated by its application to geographic atrophy in AMD, including exploration of genotype-phenotype relationships. Finally, mediation analysis will be discussed, with the aim of dissecting complex relationships between AMD disease features, genotype, and progression.