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PhD Stipend/Integrated Stipend in representation, compression, learning, and inference for classical and quantum data. At the Technical Faculty of IT and Design, Department of Computer Science, one
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analyzes video recordings to identify these subtle cues. This advancement helps therapists gain a better understanding of their clients and the psychotherapy process, ultimately improving mental health
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personalised media experience, as formulated in SMPTE ST 2110 suite of standards for video, audio and data over IP. This research asses the implications for public interest media of these fundamental changes and
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statistics This PhD project falls under the collaboration between Research Thrust RT2 Physics-based models, and Research Thrust RT3 on representation, compression, learning, and inference. For long-distance
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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms
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multimodal learning scenarios (text, speech, code), contributing to adaptive dialogue design, and examining how novice programmers develop reasoning, reflection, and metacognitive strategies when engaging with
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, Grasshopper, Python). Explore new trajectories for the advancement of AI-supported integrated architecture and its potential impact on the build environment. Contribute to developing open-source tools and code
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, such as graph-based approaches with Retrieval-Augmented Generation (RAG) and fine-tuning of LLMs. • Contribute to developing open-source tools and code repositories • Produce high-level scientific