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Field
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selection criteria Knowledge of sensors and measurement techniques Experience from the industry providing sails for merchant ships Experience with Computational Fluid Mechanics Knowledge of machine learning
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, or hold an equivalent qualification that allows enrollment as a PhD student at the University of Vienna by that date. Because we design and build our research machines ourselves, you must have extended
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field. Strong foundations in machine learning and familiarity with current AI tools and practices. A solid understanding of large language models, in particular their reliability, security, and
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, advanced digital signal processing and machine learning algorithms, the system will be designed to recognize and characterize building activities, occupancy patterns, environmental conditions, and other
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behavioural interventions in healthcare. However, current conversational AI systems remain limited in their ability to operate reliably across diverse clinical contexts. They often lack generalisability
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. The Department of Engineering (DoE) is interdisciplinary and active in the classical domains of civil, electrical, mechanical, and computational engineering. The focus of research is on the development
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at the University of Vienna, or hold an equivalent qualification that allows enrollment as a PhD student at the University of Vienna by that date. • Because we design and build our research machines ourselves, you
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years. Your work tasks This PhD project focuses on the design and control of energy-efficient hydraulic systems with integrated safety functionalities. The overall objective is to develop innovative
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to 3.5 years (full-time) for Research Doctorate (PhD) studies Remuneration: The successful applicant will receive A Research Living Allowance, at current value of $37,145 AUD per annum for PhD (2026 rate
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective