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efficient learning methods that can function under limited communication, computation, and energy constraints while ensuring privacy and security from adversarial manipulation. The project focuses
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, such as the internet of things (IoT) and cyber-physical systems (CPS). These systems require scalable and efficient learning methods that can function under limited communication, computation, and energy
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release on these projects can be found at the following link . This position’s focus is on developing engineered systems and methods to quantify the physical interactions between migrating cancer cells and
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postdoctoral researcher, you will: Assist in preparing grant applications for national and international funding opportunities. Collaborate on research projects focused on data-driven design methods, immersive
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applications for national and international funding opportunities. Collaborate on research projects focused on data-driven design methods, immersive collaborative environments, and generative AI applications
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Doctoral Researcher Heikki Mannila (Aalto) House of AI Data Analysis; Sequential and time-series data; Structure discovery, matrix decompositions; Applications of AI methods in energy systems and in
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Mannila (Aalto) House of AI Data Analysis; Sequential and time-series data; Structure discovery, matrix decompositions; Applications of AI methods in energy systems and in manufacturing industry only
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applications for national and international funding opportunities. Collaborate on research projects focused on data-driven design methods, immersive collaborative environments, and generative AI applications