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within Pioneering AI methods for integrative analysis of imaging, clinical and biomedical data to advance understanding and management of complex diseases. The position is open for appointment as of 1
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-switching valves, and hybrid fluid power systems. The professor is also expected to contribute to the development and application of model-based analysis, simulation, and optimization of fluid power and
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properties of glasses. The brittleness and low fracture toughness of various glass materials, including oxide and metal-organic framework glasses, remains to be their main disadvantage. The planned research
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the field of mechanical properties of glasses. The brittleness and low fracture toughness of various glass materials, including oxide and metal-organic framework glasses, remains to be their main disadvantage
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of the AI:EcoNet Lab. Requirements: Master’s degree in biology with a strong interest in data science Strong background in ecology and/or biogeography Experience with ecological networks, geospatial analysis and
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of study is energy planning in general and focus is on technical and geographical aspects, such as energy system analysis, and economic and institutional aspects, such as feasibility studies and public
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developing methods at the intersection of artificial intelligence and statistical genetics, with an emphasis on integrative analysis of multi-modal biomedical data. Expertise in Bayesian modelling, graphical
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integration analysis at energy system-level. The position is supported by a start-up grant to build a small research group and associated experimental facilities. However, it is expected that the applicant can
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with the perspective of translating the obtained results into digital health solutions. Sensory processing anomalies will be investigated in adults and children through recording and analysis of non
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manipulation. Specific requirements Master's degree in Biomedical Engineering, Robotics or related fields Experience with processing and analysis of electrophysiological signals Strong programming skills (Python