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, inverse problem, machine learning Expertise in the analysis of a wide range of geospatial and geodetic data sets Strong expertise in programming with Python or MATLAB As position requires stakeholder
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Electrical and Electronic Engineering, or related field. Research experience with Artificial Intelligence/Machine Learning/Large Language Model. Publication track record in a series of top tier conference
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electronics Prepare the syllabus and lecture materials for the MSc course. Meeting the learning outcomes of the MSc course. Provide reading list for the MSc course. Provide consultation to the students where
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specializing in the medical or biomedical sciences, with demonstrated excellence in both methodological research and clinical application of artificial intelligence and machine learning (ML). The candidate will
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++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with simulation environments such as Unity ML-Agents, NVIDIA Isaac Sim, Mujoco, or similar. Solid
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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Foundations & Core (Statistical) Machine Learning; Data Analytics, Engineering, Protection; and Database Systems and Principles Responsibilities: Research : The appointee is responsible for conducting high
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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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proceedings as defined in the project scope and agreements. Scientific activities include: 1) Develop Tidal Resource Models Design and implement statistical and machine learning tidal models to predict energy