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We are looking for a PhD student in Visual Learning and Communication with a focus on interactive visualization, visual learning, science communication, and educational science, formally based
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such as healthcare, finance, cy- bersecurity, and autonomous vehicles, where they interact dynamically with external knowledge sources, retain memory across sessions, and autonomously generate responses and
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects
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will include not only conducting research and publishing within your field, but also communicating and interacting with other stakeholders to place the research in a broader system perspective. As
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-based methods are particularly well-suited to bridging the molecular and experimental scales and will be central to this effort. The research will be performed in the Theory and Modelling group (https
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experience with patient interaction and knowledge of clinical study design. Further education in research, statistics, and international experience in medical institutions are considered significant advantages
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of physiological data, experience in working with natural products and knowledge of neuro-immune interactions. The applicant is expected to be fluent in both written and spoken English. Great emphasis will be given
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components to execute complex reasoning and decision-making tasks. These agents are increasingly deployed in domains such as healthcare, finance, cybersecurity, and autonomous vehicles, where they interact