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techniques for advanced computational imaging systems, combining expertise from Mathematics (Inverse Problems), Computer Science (Efficient Algorithms and High-Performance Computing), and Physics (Image
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combination of expertise in computing education research, computerized image analysis, and human-machine interaction (HMI). Within the HMI unit, the Digital Ecologies Lab is led by Prof Mike Hazas and has five
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application of machine learning algorithms to automatically classify freshwater benthic diatoms at the species level and quantify key morphological traits. These advancements aim to improve ecological
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learning, statistical analysis, and other contemporary data-driven techniques. Computational methods such as optimization, filtering algorithms, predictors, etc. Software and coding skills with, e.g., Python
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[Crockett2022], minimizing artifacts and improving clinical reliability. Meta-learning algorithms [Andrychowicz2016] will be implemented to optimize reconstruction efficiency for real-time bedside applications
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movements for enhanced training of machine-learning algorithms. 1.4. Interdisciplinarity aspects The theme of research of project FITNESS lies at the intersection of medical science (rehabilitation field) and
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of digital communications and signal processing. High grades in the core courses are required. Skills in mathematical analysis, modeling, and network algorithms are essential. Analytical thinking, eagerness
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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. The position is hosted at the Chair for Algorithms and Complexity, headed by Prof. Susanne Albers (http://wwwalbers.in.tum.de/index.html.en). The dissertation work will involve research in the fields