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career step. At HST, our values – Share. Care. Dare. – shape the way we collaborate, support one another and create knowledge with societal impact. Learn more about the department on our website and in
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration, experimental testing, or hardware-in-the-loop
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of electrolyzer technologies, digital twins, model order reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration
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spanning dairy, plant, and single-cell protein sources. How this knowledge can be used in the rational design of functional ingredients and healthy foods using established and emerging processing
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Design invites applications for a PhD stipend in the field of secure machine learning within the general study programme Electronic and Electrical Engineering; as per November 1, 2026, or as soon as
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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) or alternatively a master level in Educational Sciences, Learning Sciences, Psychology or Cognitive Science with a clear natural science perspective/interest A strong interest in STEM education, disciplinary
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connected to our activities within applied battery research, and your work will contribute to the development of new knowledge within this field. Your work tasks In this position you will conduct research