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Student or Postdoc (f/m/d) for the project Theory and Algorithms for Structure Determination from Single Molecule X‑Ray Scattering Images Project description Single molecule X‑ray scattering experiments
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for the Advancement of Surgery) initiative. The Research Associate will be responsible for developing machine learning algorithms and creating predictive models. The ideal candidate must demonstrate a robust background
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learning algorithms. • Adapting the method to multiple faults • Using the method to detect corrupt data, or even threats of attacks and intrusions on networks • Carrying out a proof of concept by
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems
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of algorithms for optimal welding (trajectory and force planning, adaptive parameters for surface/MLI, rapid data analysis: current–voltage–force–SWIR). • Data logging and synchronization, preparation
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Engineering or Industrial Engineering and Management) - 10 points; Others Masters – 2 points) b) Experience in applying machine learning algorithms, data preparation, normalization, feature selection, and
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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. More information about DESS can be found at: https://www.cs.aau.dk/research/Data-Engineering-Science-and-Systems. The Center for Classical Communication in the Quantum Era (CLASSIQUE) is a pioneering
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . The research associate will focus on Vision-Language Model based situation awareness and decision-making
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co