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Field
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profile: A PhD in AI, preferably at the interface of information retrieval and machine learning; Research background in generative information retrieval, with publications in the leading venues relevant
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Large Language Models are known to hallucinate. Techniques such as In-Context Learning, Retrieval-Augmented Generation, Chain-of-Thought Prompting, and Self-Refinement can help reduce hallucinations, but
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community). What we ask of you We are looking for enthusiastic and curious candidates who meet the following profile: A PhD in AI, preferably at the interface of information retrieval and machine learning
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shown by PhD related outputs) expertise applying creative learning methods, e.g. artistic methods familiarity with working in international environments and within networks collaboration, pro-activity
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Learning, Retrieval-Augmented Generation, Chain-of-Thought Prompting, and Self-Refinement can help reduce hallucinations, but they do not eliminate them entirely. This poses a challenge in applications where
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strategies in psychiatry. This research combines recent advances in machine learning and cognitive neuroscience to contribute to future clinical tools for diagnosing and monitoring neuropsychiatric disorders
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electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is to utilise material platforms that exhibit multiwell behaviour
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Employment 1.0 FTE Gross monthly salary € 3,546 - € 5,538 Required background PhD Organizational unit Faculty of Science Application deadline 09 November 2025 Apply now The goal of the project is to
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Employment 1.0 FTE Gross monthly salary € 4,728 - € 6,433 Required background PhD Organizational unit Faculty of Social Sciences Application deadline 19 October 2025 Apply now Are you passionate
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for cognitive science and artificial intelligence, including about 35 PhD students. Core research domains include cognitive science, machine learning, deep learning, games, virtual reality, computational