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effective use of our research computing systems and application software through training and education, consultation, and documentation contribute to the discovery process through algorithm design and
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will be processed by advanced Artificial Intelligence algorithms. The numerous tasks for this grant will contribute to the project's activities. Namely: Installation of sensors on telecommunications
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will be processed by advanced Artificial Intelligence algorithms. The numerous tasks for this grant will contribute to the project's activities. Namely: Installation of sensors on telecommunications
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: Miguel Sá Sousa Castelo Branco IV - Work Plan / Goals to be achieved: To develop and test algorithms that can provide neurofeedback in real time from neurophysiological data. V - Initial grant duration: 12
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is to discover governing equations from experimental data to generate mathematical models of cellular signaling dynamics. You will help design algorithms for data-driven model discovery, test proposed
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algorithms that allow robots to refine their control strategies based on observed human behaviour. Collaboration: The project will benefit from extending existing collaborations between the University
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research in neuro-symbolic AI, with a focus on using generative and agentic AI, as well as AI standards to create trustworthy information resources. This includes the design of algorithms, tools, and process
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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
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, high school timetabling, examination timetabling, master thesis defense assignments and scheduling and student project and master’s thesis assignments. For these problems, we wish to develop an algorithm
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate