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LiDAR data (GEDI and ICESat-2), and generate time series; e) Apply deep learning (DL), machine learning (ML), geospatial foundation models (e.g., AlphaEarth, TESSERA), statistical inference (uncertainty
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programming, and/or human-computer interaction (UX), and/or application of Machine Learning. Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. 3. Financial
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Engineering, or related fields. They must have proven experience in the development and programming of Information Systems; prior experience in applications based on Data Analytics/Machine Learning and data
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-computer interaction (UX), and/or appli cation of Machine Learning. • Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. Financial component
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of Conception and Drawing of Mechanical Components and Systems, Product Development, Computer-Aided Drawing and Manufacturing, Computer Graphics, Image Processing and Analysis and Scientific Visualization. Work
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(AutoML) platform to output Machine Learning/Deep Larning models focused on image analysis. In this sense, the focus will be the development of several modules: a module to load, process and store images
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; Firmware development; Machine Learning concepts and tools applied to Edge-AI. Preferred conditions: R&D experience in the area in which the competition is open will be valued. Work plan: The activities to be
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machine learning using chemical compounds— information provided in the CV and/or motivation letter; Knowledge of the Python programming language — information provided in the CV and/or motivation letter
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learning university. In this competition, experience in research and/or development in Large Language Models (LLM) and their respective applications is valued. Where to apply Website https
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16 Apr 2026 Job Information Organisation/Company Universidade do Porto – HR Department Human Resources Department Research Field Engineering » Computer engineering Researcher Profile Other