Head of a new research group and member of the Centre Board for a Senior Lecturer Autonomous Learning Systems and Reinforcement Learning (incl. responsibility in research & leadership) 50 - 100 %.

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.

Head of a new research group and member of the Centre Board for a Senior Lecturer Autonomous Learning Systems and Reinforcement Learning (incl. responsibility in research & leadership) 50 - 100 %.

Open professorship in Industrial AI with a focus on Neurosymbolic Learning & Reasoning. The role involves shaping research and teaching in the field of neurosymbolic approaches for industrial applications internationally and forming our environment in a lasting way.

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.

The SAIL fellowship program is looking for postdocs and advanced researchers who want to continue and expand their research in line with the SAIL research agenda at Universität Bielefeld or Universität Paderborn for a short fellowship (up to 3 months). The program is aimed at enriching the research carried out in SAIL, supporting research ties with relevant communities, and establishing long-term collaboration with institutes across the globe. The fellowships are intended to strengthen the innovation potential of researchers with expertise in the field of AI through further training and interdisciplinary collaboration within the research network.

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.

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.

We are seeking highly motivated and talented PostDoc and PhD-candidates to join our dynamic research team for combining symbolic and sub-symbolic AI. It offers a unique opportunity to create a new level of artificial intelligence. The successful candidates will conduct research in collaboration with all partner institutes JKU, AAU Klagenfurt, ISTA, TU Graz, TU Vienna, and WU Vienna.

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the existing literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.

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.

The eye is a multi-component biological system, where mechanics, optics, transport phenomena and chemical reactions are strictly interlaced, characterized by the typical bio-variability in sizes and material properties. The eye’s response to external action is patient-specific and it can be predicted only by a customized approach, that accounts for the multiple physics and for the intrinsic microstructure of the tissues, developed with the aid of forefront means of computational biomechanics. Our activity in the last years has been devoted to the development of a comprehensive model of the cornea that aims at being entirely patient-specific. While the geometrical aspects are fully under control, given the sophisticated diagnostic machinery able to provide a fully three-dimensional images of the eye, the major difficulties are related to the characterization of the tissues, which require the setup of in-vivo tests to complement the well documented results of in-vitro tests. The interpretation of in-vivo tests is very complex, since the entire structure of the eye is involved and the characterization of the single tissue is not trivial. The availability of micromechanical models constructed from detailed images of the eye represents an important support for the characterization of the corneal tissues, especially in the case of pathologic conditions. In this presentation I will provide an overview of the research developed in our group in terms of computational models and experimental approaches developed for the human cornea.