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the development of high-fidelity multiphysics models of existing research infrastructure, as well as novel system architectures and emerging energy concepts under investigation. A central component of the position
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generation of 10 researchers in cybersecurity and resilience for cyber-physical systems (CPS). The 10 doctoral candidates will collaborate on modeling threat actors, developing scalable AI-based monitoring and
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. • Design, implement, and train machine learning models for tasks such as object detection, recognition, and image segmentation. • Develop scalable training pipelines and optimize models for real-time or
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generation of 10 researchers in cybersecurity and resilience for cyber-physical systems (CPS). The 10 doctoral candidates will collaborate on modeling threat actors, developing scalable AI-based monitoring and
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on conventional yellow sticky traps. MANIFESTO team will explore advanced and modern Deep Learning (DL) architectures to develop an accurate model for detection and classification. Moreover, the project aims
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architectures. This includes among other: (a) design and implementation of machine learning and GenAI models, (b) efficient training and inference on GPU-based systems, (c) fine-tuning and optimization of large
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enhance the competencies of the Institute in one or more of the following research areas: foundation models and efficient training/adaptation methods on HPC systems, generative AI and multimodal learning
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ACP’s captured instances on conventional yellow sticky traps. MANIFESTO team will explore advanced and modern Deep Learning (DL) architectures to develop an accurate model for detection and classification