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Field
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Knowledge in the field of Machine Learning, including training, inference, and optimisation of transformer architectures Knowledge in the field of ML security is desirable. Good Python skills, especially with
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present. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python skills, preferably some
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learning • Programming skills (e.g., Python, R) • Interest in interdisciplinary research at the intersection of data analysis and learning sciences • Interest in education and learning as an
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Transformers). Analysis of existing datasets. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python
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of workflows in Python to optimize throughput, reproducibility, and data management. The overarching goal is to create a standardized and scalable platform that accel-erates discovery in stem cell biology and
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• Proficiency in statistical or programming tools (e.g., R, Python) • Interest in education and learning as an application domain • Ability to work independently • Demonstrated academic writing
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systems and basic knowledge in information theory ▪ Proficiency in at least one programming language (e.g. Python) ▪ Interest in AI‑based attack models and security research The following points
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a related field ▪ Strong knowledge in wireless communication systems, signal processing, or radar systems ▪ Proficiency in at least one programming language (e.g. Python) ▪ Interest in hands
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project-specific requirements. Students can deepen their knowledge about selected topics (e.g. Bayesian Statistics, HMMs, AI, advanced programming in Python) in small classes of max. 10 participants
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Manipulation in Cluttered and Dynamic Environments (ID: TUEILSY-PHD20240930-SCMM) A more detailed topic description can be found at https://www.ce.cit.tum.de/lsy/open-positions/open-phd-positions/ . Requirements