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| deniz_cetinkaya [2026/10/16 14:36] – Deniz Cetinkaya | deniz_cetinkaya [2026/10/16 14:39] (current) – Deniz Cetinkaya |
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| Email: [[dcetinkaya@bournemouth.ac.uk]] | Email: [[dcetinkaya@bournemouth.ac.uk]] |
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| {{tag>"MACHINE LEARNING" "QUANTUM COMPUTING" "DATA SCIENCE" "COMPUTER SCIENCE" "SOFTWARE ENGINEERING" "SOFTWARE DEVELOPMENT" "ARTIFICIAL INTELLIGENCE" }} | {{tag>"MACHINE LEARNING" "QUANTUM COMPUTING" "DATA SCIENCE" "COMPUTER SCIENCE" "SOFTWARE ENGINEERING" "SOFTWARE DEVELOPMENT" "ARTIFICIAL INTELLIGENCE" "Generative AI" "Agentic Systems" "Intelligent Systems" "QUANTUM ALGORITHMS" }} |
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| This project explores the application of agent-based modelling and AI to simulate, analyse, and improve business processes within an engineering company. The student will investigate how process simulation and AI-assisted analysis can identify inefficiencies, evaluate alternative scenarios, and support data-driven process improvement. An interest in simulation, AI, process modelling, and optimisation would be beneficial. | This project explores the application of agent-based modelling and AI to simulate, analyse, and improve business processes within an engineering company. The student will investigate how process simulation and AI-assisted analysis can identify inefficiencies, evaluate alternative scenarios, and support data-driven process improvement. An interest in simulation, AI, process modelling, and optimisation would be beneficial. |
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| **3. Evaluating Large Language Models (LLMs) for Mental Health Applications: Risks, Ethics, and Responsible Use** | **3. Evaluating LLMs for Mental Health Applications: Risks, Ethics, and Responsible Use** |
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| This project investigates the use of different large language models (LLMs) in the context of mental health, focusing on their capabilities, limitations, and potential risks. The project will involve developing an interactive dashboard to facilitate experimentation with different models, enabling their outputs and performance to be explored and compared. The student will systematically evaluate model responses to mental health-related scenarios, examining issues such as accuracy, reliability, bias, potentially harmful outputs, and ethical concerns. | This project investigates the use of different large language models (LLMs) in the context of mental health, focusing on their capabilities, limitations, and potential risks. The project will involve developing an interactive dashboard to facilitate experimentation with different models, enabling their outputs and performance to be explored and compared. The student will systematically evaluate model responses to mental health-related scenarios, examining issues such as accuracy, reliability, bias, potentially harmful outputs, and ethical concerns. |