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Field
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systems that fuse multi-modal remote sensing, soil and phenology data to enable crop classification and precise, customized fertilizer recommendations. Selection criteria (short) Required PhD (awarded
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of artificial intelligence for healthcare applications, medical image analysis, and human-centered computing systems, with a focus on cross-domain transfer learning and multi-modal data integration. Formulate
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partnerships with industry and local stakeholders. Project summary (one line) Develop farmer-centric systems that fuse multi-modal remote sensing, soil and phenology data to enable crop classification and
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research projects in aquaculture (e.g., health monitoring, yield prediction, system optimization) and/or drug delivery (e.g., predictive modeling, nanocarrier design, therapeutic response analysis). Develop
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term) will study how different sensory modalities (vision, audition, olfaction) give rise to the same social concepts, combining neuroimaging and neurophysiology. These approaches will constitute a first
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Aquaculture – 30% Experience in zootechnics and fish broodstock management– 30% Experience in gamete quality analysis techniques- 20% Experience in translation of teaching contents – 10% Portuguese level – 10
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of clinical decision support systems and multimodal analytic platforms to translate advanced data science solutions into critical care. The main focus of this position is on the analysis of the signals
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work with amazing partners, including other Harvard Medical School-affiliated hospitals. The qualified candidate will specifically focus on the analysis of large genomics datasets derived from cancer
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fields data and plenoptic data. Work plan: The grant holder will develop models for representation of radiance fields and plenoptic data, and for its coding and quality analysis. Furthermore it is expected
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incorporates the use of Artificial Intelligence tools to evaluate and adapt knowledge management practices. The candidate will conduct systematic literature reviews, critical analysis, data collection through