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Field
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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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). 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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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning
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assimilation, and machine learning frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well
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of establishing relationships between signal sources and predicting commands; 6. Design of machine learning and adaptive models that ensure the continuous evolution of the system, increasing the autonomy and
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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
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and thermal-aware design techniques for embedded systems. Contribute to research outputs and support timely completion of project milestones. Job Requirements: Preferably PhD in Computer Engineering
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Engineering, Biomedical Engineering, Information Systems, Artificial Intelligence or related areas that demonstrate a solid computational base; Candidates enrolled in a non-degree course: Candidates who exceed
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of education and related technical/military/paraprofessional experience may be substituted for a bachelor’s degree on a year for year basis. An advanced degree (Masters or PhD) may be substituted
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machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy. Research directions will be flexible and shaped according to mutual