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Virtualization and Virtual Machines
- Very low-resource machines
- HW and SW architectures
- Tiny Embedded Systems and Material-integrated Intelligent Systems (MIIS)
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Distributed Artificial Intelligence
- Multi-agent Systems: Programming, Technologies, Platforms
- Self-* Systems (Self-organization, Self-adaptation, Self-awareness, Self-modification, ..)
- Distributed Machine Learning
- Ensemble Learning (local state estimation, global state fusion)
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Machine Learning
- Robust Learning on (noisy) Sensor Data
- Learning on low-resource and embedded Systems, Tiny & Edge ML
- Multi-instance and Ensemble Learning
- Damage Diagnostics and Classification
- Process Optimisation
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Agents
- Agent-based Modelling (ABM)
- Agent-based Computing (ABC)
- Agent-based Simulation (ABS)
- All together (ABX)
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Simulation Research
- Large-scale multi-agent simulation (> 1000 Agents)
- Multi-domain and multi-scale Simulation (e.g., combining agent and physical simulation)
- Real-world in the loop Simualtion (Augmented Virtuality by combining real and virtual worlds in simulation)
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Practical and Applied Machine Learning
- Incremental and Stream Learning
- Learning with noisy and unreliable data
- Distributed Learning
- Agent-based Learning
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Massive parallel and distributed systems
- Algorithmic driven Complex System-on-Chip Design (High-level Synthesis)
- Dependable Embedded Systems
- Distributed Sensor Networks and SHM
- Smart Adaptive Materials and Structures
- Sensor Aggregation and Data Fusion
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Computer Architecture
- Agent Processing Platforms
- System-on-Chip Design
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Materials Informatics
- Computing WITHIN Materials
- Algorithmic Scaling
- Hardware Design
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Crowd Sensing and Social Sciences
- Data Mining
- Machine Learning
- Aggregation and Data Fusion
- Environmental Monitoring (e.g. Earthquake Monitoring with Crowd Sensing)
- Software Technologies
- Practical Programming Languages (Design)
- Compiler Design
- High-level Synthesis and HW-SW-co design