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Field
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graph learning models, primarily geared towards assisting combinatorial solvers for practical graph algorithm benchmarks. Please find out more here: Dr. G. Rattan Information and application Are you
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motivated and self-driven PhD student with strong intellectual curiosity and interest in computational mechanics, data-driven design, and sustainable engineering. If this excites you and you want to make a
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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and
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analytics, anomaly detection, and embedded redundancy to enhance system resilience. Students will focus on creating adaptive algorithms and hardware implementations that enable real-time diagnostics and
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for image classification and other domains against existing open source LLM (e.g., Llama 3, Phi-3), as well as develop new kinds of attacks, for example based on evolutionary algorithms. 2. Investigate
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, epidemiological data and outcome modelling using AI-assisted algorithms, as well as multi-modal data integrations. An established data infrastructure with expertise and computational pipelines for these analyses
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must have (or will receive in a few months) a Master’s degree in Computer Science Engineering, Master of Science in Information Engineering Technology, Master of Science in Computer Science, Master of
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propagation models that incorporate the effects of fire effluents, validated through controlled experimentation. You will develop tomographic inversion methods and anomaly-detection algorithms capable
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Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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