Data Science and AI

Strategic and fundamental research is performed on novel AI techniques and paradigms, including:

• Neuro-Symbolic AI, combining the strengths of symbolic reasoning and neural networks, to develop more explainable and interpretable AI systems that can handle complex knowledge representation and reasoning tasks.
• Neuroscience-Inspired AI, inspired by the structure and function of the human brain, using principles from neuroscience to design more efficient and effective algorithms for learning, decision-making, and perception.
• Physics-informed AI aims to better predict complex physical systems. By incorporating fundamental physical laws and constraints into machine learning algorithms, physics-informed AI can improve the accuracy, robustness, and interpretabili

AI model and architecture design for (distributed) systems and processes that support:

• Data-Efficient AI – achieving high performance with minimal amounts of training data.
• Resource-constrained AI – to operate effectively with limited computational resources.
• Explainable & trustworthy AI – to be easily understood and audited by humans, while operating in an ethical and transparent manner.
• Distributed AI & edge computing – processing and analysing data in a decentralized manner, closer to the source of the data.
• Data privacy for AI – ensuring that individuals have control over their personal data.
• AI for robotics – Enabling robots & AGVs to operate in real world environments and support people with physical tasks.

Covering challenges withing the domains of Healthcare – Pharmaceuticals – Industry 5.0 – Agriculture – Energy – Media – Mobility – Bioinformatics – Smart Homes / Cities – Logistics – Cybersecurity – Networks & Telecommunications – Circuit Design Within these domains, data analysis and ML techniques are applied on (a combination of):

• Time Series Data: event detection, predictive modelling, causality inference,
• Textual Data: information extraction, text generation,
• Image/Video Data: compression, object detection, XR and holographic-type video delivery,
• OMICS Data: data sequencing, molecular biomarker identification, drug discovery,
• Speech/Audio data: speech enhancement, scene analysis, information extraction,
• Networks data: characterisation of RF environments, wireless-wired networks analysis & management, network intrusion detection