195 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" positions at Technical University of Munich in Germany
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research and travel budget available to best support your research. You will partic-ipate in teaching and supervising students, interact with and learn from the other team members, and re-ceive close
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning
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Linguistics, Data Science or a similar field Good theoretical knowledge and practical experience with Natural Language Processing (rule-based and/or machine learning) Software Engineering Motivation to build
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the topic: 1. M Balaish, JLM Rupp, Widening the Range of Trackable Environmental and Health Pollutants for Li‐Garnet‐Based Sensors, Advanced Materials, 2021; https://doi.org/10.1002/adma.202100314 2. M
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systems with more than two energy levels (qudits) Scaling compilation approaches to utilize the processing power of parallel systems/supercomputers To learn more about our previous work, please check out
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training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and familiarity with machine learning
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(MDSI) is an integrative research institute at the Technical University of Munich (TUM), with an interdisciplinary and cross-faculty focus on data science, machine learning, and artificial intelligence
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skills, ability to interact with scientists at different levels good software design skills and the ability to write clean, and reusable code in machine learning, deep learning frameworks, such as
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on the nanoscale (previous works e.g.: https://www.nature.com/articles/s41563-019-0555-5). You will also supervise one PhD student who will work on a complementary topic guaranteeing quick output and an ideal