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.

A scholarship of the Italian National PhD Program in Artificial Intelligence is available at the Institute of Cognitive Science and Technologies of the National Research Council in Rome. The research topic is “Self-organisation and learning in massive multiagent systems and robot swarms” with the supervision of Stefano Nolfi.

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 PhD student will be part of the ANR project 'REFINED' involving the Laboratory of Embedded Artificial Intelligence in CEA List in Paris, the Multispeech research team In LORIA, Nancy, and the Hearing Institute in Paris. The project aims at studying new Deep Learning based methods to improve hearing acuity of ANSD patients. A cohort of ANSD volunteers will be tested to identify spectro-temporal auditory and extra-auditory cues correlated with the speech perception. Additionally, the benefits of neural networks will be studied. However, current artificial intelligence methods are too complex to be applied to processors with low computing and memory capacities: compression and optimization methods are needed.

The artificial intelligence lab of the Vrije Universiteit Brussel (Brussels, Belgium) is looking for candidates for a full-time professor position.

The artificial intelligence lab of the Vrije Universiteit Brussel (Brussels, Belgium) is looking for candidates for a fully funded PhD position in agent-based modelling of the evolution of speech.

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.

The University of Texas at San Antonio (UTSA), MATRIX AI Consortium, invites applications for the position of Full Professor / Associate Professor, to be appointed as a University of Texas System (UT System) Research Excellence Regents' Professor. Successful candidates will be part of a strategic Clustered & Connected Hiring Program (CCP) focused on Artificial Intelligence, with an anticipated start date in the Fall of the 2025-26 academic year. The University of Texas System recently approved the creation of the Regents’ Research Excellence Program across its four Emerging Research Universities (ERUs), including UTSA. UT System has allocated $55 million across all four ERUs to fund the recruitment of research-active faculty to dramatically grow its national research prominence and federal funding opportunities. The 5 positions open are in areas: Trustworthy AI/ML Algorithms (eg: neuro-inspired algorithms), Neuromorphic AI Accelerators/Chips, Human-Centered AI, AI Ethics, Quantum Encryption for AI Confidentiality. The Regents Professors will be core members of the MATRIX Consortium, which is a central hub for 87 AI scientists, facilitating transdisciplinary research, fostering high-impact collaborations, and offering thought leadership and domain expertise to address the most challenging and complex problems in AI. Areas of interest include Trustworthy AI/ML Algorithms, Neuromorphic AI Accelerators, Human-centered AI, AI Ethics, all of which advance the research thrusts in the MATRIX. MATRIX strives for scientific excellence in developing holistic solutions for human well-being. The team has a successful track record in securing large collaborative grants that generated multiple centers, such as the NSF AI Partner Institute, two NSF EFRI BRAIDs, AFOSR COE in neuro-inspired AI, along with large collaborative projects in AI for healthcare.

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.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

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.