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Field
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) scientific studies Experience with relevant field data collection methods (e.g. chamber- or eddy covariance-based C flux measurements, biodiversity sampling methods) Computer programming skills (e.g. Matlab, R
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, enhance or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along the composition-process
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related to human-computer interaction and software engineering. Your competencies Applicants should have a strong interest in human-robot interaction and the role of emerging technologies in healthcare
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, to achieve spatial understanding and cross-modal representation learning from heterogeneous sensor data, with the research not limited to these methods. This research will support semantic interpretation
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with, enhance or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along the composition
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economics Economics » Econometrics Engineering » Computer engineering Information science » Information management Mathematics » Applied mathematics Mathematics » Statistics Computer science » Programming
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human-computer interaction, computer science, software engineering, digitalization and application development, or a related discipline. Experience with user studies and design methods will be considered
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package on archetype generation and develop data integration pathways Contribute to estimating environmental exposures at archetype levels using computational methods like fluid dynamics, AI and ML
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potential of classical and innovative fertility forecasting methods in the context of the Czech Republic Profile of the graduate In addition to the scientific and pedagogical pathway at universities, a
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5