In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep ...

Machine learning, with its ability to analyze large datasets and identify patterns, is particularly well-suited to address the challenges presented by the vast and complex data generated in ...

Machine learning is rapidly emerging as a pivotal tool in plant tissue culture research, offering innovative approaches to optimise protocols, predict morphogenic responses, and streamline ...

One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule's properties, such as its boiling or melting point. Once researchers can pinpoint that prediction, ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Gain a deeper understanding of artificial intelligence with Machine Learning Fundamentals: Principles and Applications. This course explores core concepts and practical uses of supervised and ...

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...

Baritinib is a drug to treat rheumatoid arthritis. After outbreak of COVID-19, Benevolent AI Company discovered that AAK1 is a regulatory factor in the process of SARS-CoC-2 infection by generating a ...

Industrial automation is entering a new era with physical AI, where machine learning meets real-world motion control. AI-driven robotics and digital twins are closing the gap between simulation and ...

Are you contemplating a PhD and interested in economic or social science applications of machine learning? You might be a good fit for our pre-doc position. The Center for Applied Artificial ...

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