1) Lecturer/Senior Lecturer (Assoc/Asst Prof) in Machine Learning: The University of Manchester is making a strategic investment in fundamentals of AI, to complement its existing strengths in AI applications across several prominent research fields in the University. Applications are welcome in any area of the fundamentals of machine learning, in particular probabilistic modelling, deep learning, reinforcement learning, causal modelling, human-in-the-loop ML, explainable AI, ethics, privacy and security. This position is meant to contribute to machine learning methodologies and not purely to their applications. You will be located in the Department of Computer Science and, in addition to the new centre for Fundamental AI research, you will belong to a large community of machine learning, data science and AI researchers. 2) Programme Manager – Centre for AI Fundamentals: The University of Manchester is seeking to appoint an individual with a strategic mindset and a track record of building and leading collaborative relationships and professional networks, expertise in a domain ideally related to artificial intelligence, excellent communication and interpersonal skills, experience in managing high-performing teams, and demonstrable ability to support the preparation of large, complex grant proposals to take up the role of Programme Manager for the Centre for AI Fundamentals. The successful candidate will play a major role in developing and shaping the Centre, working closely with its Director to grow the Centre and plan and deliver an exciting programme of activities, including leading key science translational activity and development of use cases in the Centre’s key domains, partnership development, bid writing, resource management, impact and public engagement strategies.

SPKI is expanding, and is in search of a highly motivated data scientist / ML engineer who wants to contribute to the development and implementation of new artificial intelligence (AI) tools for health. The work will be done in a highly interdisciplinary environment, and you will collaborate with a team consisting of clinicians, scientists from the university and technologists, legal experts, industry partners, as well as personnel responsible for ICT, data security, privacy concerns and more. This environment also includes researchers at The Machine Learning Group and Visual Intelligence.

The University of Wisconsin-Madison's Information School seeks highly qualified candidates for up to two tenured positions in information sciences. These faculty positions will be academic nine-month, tenure-track appointments at the Associate Professor level, to start August 2024. Applications at the Professor level may be considered in exceptional cases. Applications are specifically encouraged in, but not limited to, the following areas: Natural language processing and information retrieval, e.g., applied natural language processing, text analysis, text and multimedia retrieval, recommendation systems, conversational systems. Computational social sciences, e.g., analytics and modeling of political behavior; computational analysis of social networks; algorithms and social media analytics; social simulation of organizational behavior. Policy analysis or policy-making studies of information or data security/risk/assurance, privacy, data governance, or data management. ML/AI, computation, and the future of work. Computational and information technologies in relation to children and/or elderly populations.

We are looking for PhD students at the University of Edinburgh for research focused on: Machine learning and optimization algorithms, Generative AI and artificial data, Privacy, fairness and explainability, Topological and Geometric data analysis and other similar areas.

Synthetic face datasets are increasingly used to overcome the limitations of real-world biometric data, including privacy concerns, demographic imbalance, and high collection costs. However, many existing methods lack fine-grained control over identity attributes and fail to produce paired, identity-consistent images under structured capture conditions. In this talk, I will present FLUXSynID, a framework for generating high-resolution synthetic face datasets with user-defined identity attribute distributions and paired document-style and trusted live capture images. The dataset generated using FLUXSynID shows improved alignment with real-world identity distributions and greater diversity compared to prior work. I will also discuss how FLUXSynID’s dataset and generation tools can support research in face recognition and morphing attack detection (MAD), enhancing model robustness in both academic and practical applications.

This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.