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Field
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ML models and methodologies for the analysis of GVMs. Possible Research Directions: Synthetic Data, Evaluation Frameworks and Benchmarks for AML LLMs for AML Case Understanding Graph Neural Networks
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Models, Knowledge Graphs, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the following areas: Natural Language Processing, Knowledge Graphs, Machine
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Transmission and processing of live measurement data from laboratory setups, as well as storage in databases, and live analysis and visual presentation Active participation in innovative projects based on open
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Support with data analysis and scientific visualization Your profile: The successful candidate should have experience with computing for physics applications. In particular the following points are required
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graph neural network that predicts condition-specific protein-protein interactions. High-confidence predictions are expected to feed into protein function classification, shedding light on
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formats, ensuring data quality and plausibility, linking entities across sources, integrating spatial and demographic information, and providing structured access for analysis and visualization. This thesis
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Integration of simulation and measurement data into interactive or VR environments Data processing, signal analysis, and visualization (e.g. in Python / MATLAB) You are not expected to be an expert in all three
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) to convert images of schematics of technical building systems into digital models. The results can then be visualized, further edited, and exported. In the background, the digitized system models
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is internationally recognized for its expertise in applied electroacoustics and audio engineering, AI-based signal analysis and machine learning, and data privacy and security. Under the motto 'Where
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science, or a comparable field. You already have experience in Linux systems (Shell/Terminal), Python (e.g., for data analysis with pandas) as well as statistical methods, and Git. Knowledge in Modbus-RTU