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through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and
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and trustworthiness of AI-assisted software changes The work combines methods from software engineering, data analysis, and AI, and includes both conceptual development and empirical validation. We
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development processes Exploring techniques for improving the reliability and trustworthiness of AI-assisted software changes The work combines methods from software engineering, data analysis, and AI, and
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the following programming languages/environments: Python, PyTorch (or similar), and experience with LLM frameworks (e.g., Hugging Face, LangChain) or data analysis tools. Hold a Master of Engineering or
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in one or more of the following programming languages/environments: Python, PyTorch (or similar), and experience with LLM frameworks (e.g., Hugging Face, LangChain) or data analysis tools. Hold a