Complex biological systems are more than the sum of their parts – their properties emerge from the dynamic interaction of their components, such as molecules or cells. PhD researchers now have the opportunity to develop their own theoretical perspective on these systems as part of an international Doctoral Network. A European consortium initiated by researchers from the University of Göttingen, the Max Planck Institute for Dynamics and Self-Organization (MPI-DS), and the University of Edinburgh has been awarded €4.5M by Marie Skłodowska-Curie Actions to coordinate the network. The network consists of twelve European universities and research centres along with a number of partners outside academia. It is coordinated by the University of Edinburgh.

The University of Texas at San Antonio launched the College of AI, Cyber and Computing on Sept. 1 bringing together academic programs in artificial intelligence, cybersecurity, computing and data science. The new college positions UT San Antonio at the forefront of technological education and research and brings the number of academic colleges at UT San Antonio to nine.

Scientists from Auburn University have proposed a new mechanism to control some of the thinnest electronic memory devices ever made. Their study uncovers how tiny crystals only a few atoms thick may switch between insulating and metallic states, paving the way for low-power memory, flexible electronics, and brain-inspired computers.

A research team from the Hong Kong University of Science and Technology (HKUST), led by Professors Gan Jianping (Department of Ocean Science) and Professor Yang Can (Department of Mathematics), has developed a novel AI-powered tool named STIMP for diagnosing coastal ocean productivity and ecosystem health. STIMP introduces a novel paradigm that imputes missing data and then predicts Chlorophyll-a (Chl-a) concentrations across large spatiotemporal scales. In tests across four representative global coastal regions, STIMP significantly outperformed existing geoscience tools, reducing the mean absolute error (MAE) for imputation by up to 81.39% and for prediction by 58.99%. Accurate Chl-a prediction aids in early detection of harmful algal blooms, ecosystem protection, and provides data-driven insights for evidence-based policy-making.