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focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be designed and deployed efficiently
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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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data sources (e.g., registry data, surveys, and organisations). Your competencies Digital methods such as machine learning based classification, computational text analysis, network analysis, web
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industrial energy systems that combine physics and data to become adaptive, autonomous and trustworthy? To get there, you will work at the intersection of thermal energy systems, machine learning and
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collaboration with a leading architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction
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electrical engineering, control engineering, applied mathematics, computer science, or a related field A strong background in probability and statistics, machine learning, or control theory Interest in cyber
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implement a hyperspectral imaging system tailored to bulk forensic trace analysis and develop chemometric and machine-learning models for material identification and classification. You will evaluate
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the implications of generative and agentic AI for areas such as leadership, everyday work practices, team communication, professional learning, careers, and inclusion. Doctoral candidates become part of
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calls. New research areas may be added until the application deadline. Beyond the research conducted during the PhD project, a successful candidate is expected to teach three to four hours weekly during