The Cyprus Institute invites applications for a highly qualified and motivated individual to join the Institute as a Postdoctoral Research Fellow in Data-Driven Computational Science at CaStoRC. The successful candidate will conduct fundamental research in one or more of the following areas: Data mining methods, Complex network analysis, Deep learning architectures, Cross-disciplinary applications of “big data” methods in climate science, smart farming, education, health, etc. The successful candidate will also work closely with the PI in writing relevant grant proposals.

We are currently looking for a university assistant (full-time doctoral student for up to 4 years) for the Computational Data Analytics group of Prof. Johannes Fürnkranz. We are particularly interested in researchers who will strengthen our expertise in one or more of the following areas: Machine Learning and Game Playing, Symbolic Machine Learning, Machine Learning and Logic, Inductive Rule Learning, Interpretable AI, Data Mining and Knowledge Discovery.

At Sony AI, we are searching for a (Senior) Research Scientist Data Mining/Knowledge Discovery & ML to join one of our offices in Barcelona (preferred), Zurich or Tokyo. The role involves working with a highly diverse, international team of scientists and engineers pushing the boundaries of AI/ML research.

The School of Computer Science in the Gallogly College of Engineering at the University of Oklahoma seeks applications for a tenure-track assistant professor position in the area of hybrid machine-learning/first-principles modeling with a targeted start date of Fall 2024. Candidates are expected to leverage existing first-principles modeling strengths across GCoE as well as existing strengths in machine learning and data mining in CS. The role involves supporting OU's strategic research verticals and engaging in collaborative research. The position also includes teaching responsibilities, advising M.S. and Ph.D. students, and contributing to the advancement of the community.

The School of Applied Mathematics at Fundação Getulio Vargas (FGV EMAp) in Rio de Janeiro, Brazil, invites applications for one open-rank faculty position in Data Science to strengthen and complement our existing research activity in this area. We are looking for established researchers (associate/full professor) or outstanding young researchers (assistant professor) who have demonstrated research and teaching expertise in Data Science. We will prioritize applicants whose research focuses on natural language processing, computer vision, reinforcement learning, network science and data mining, but we also welcome applications from other fields in Data Science. The successful candidate is expected to develop an externally funded research programme, publish in high-impact venues, supervise research (postgraduate) students, teach at both undergraduate and graduate levels, and provide service to the department and institution. In general, teaching duties consist of two courses per year, one at Undergraduate and one at Graduate level. Peer-reviewed external funding is expected to be obtained and sustained. Industrial partnerships are also strongly encouraged.

An understanding of the self helps explain not only human thoughts, feelings, attitudes but also many aspects of everyday behaviour. This talk focuses on a viewpoint - self as processes. This viewpoint emphasizes the dynamics of the self that best connects with the development of the self over time and its realist orientation. We are combining psychological experiments and data mining to comprehend the stability and adaptability of the self across various populations. In this talk, I draw on evidence from experimental psychology, cognitive neuroscience, and machine learning approaches to demonstrate why and how self-association affects cognition and how it is modulated by various social experiences and situational factors

1. Embedding is all you need: A machine learning way to analyse white matter tractography streamlines - Dr Shenjun Zhong, Monash Biomedical Imaging Embedding white matter streamlines with various lengths into fixed-length latent vectors enables users to analyse them with general data mining techniques. However, finding a good embedding schema is still a challenging task as the existing methods based on spatial coordinates rely on manually engineered features, and/or labelled dataset. In this webinar, Dr Shenjun Zhong will discuss his novel deep learning model that identifies latent space and solves the problem of streamline clustering without needing labelled data. Dr Zhong is a Research Fellow and Informatics Officer at Monash Biomedical Imaging. His research interests are sequence modelling, reinforcement learning and federated learning in the general medical imaging domain. 2. Application of artificial intelligence in correcting motion artifacts and reducing scan time in MRI - Dr Kamlesh Pawar, Monash Biomedical imaging Magnetic Resonance Imaging (MRI) is a widely used imaging modality in clinics and research. Although MRI is useful it comes with an overhead of longer scan time compared to other medical imaging modalities. The longer scan times also make patients uncomfortable and even subtle movements during the scan may result in severe motion artifact in the images. In this seminar, Dr Kamlesh Pawar will discuss how artificial intelligence techniques can reduce scan time and correct motion artifacts. Dr Pawar is a Research Fellow at Monash Biomedical Imaging. His research interest includes deep learning, MR physics, MR image reconstruction and computer vision.