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for coastal ecosystems. You will perform statistical analyses of time series and spatial data on ecosystems and human activities and participate in more holistic analyses of socio-ecosystems. You will also
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-Class Environment: Access to a leading research environment specializing in hardware/software for medical wearables, translational endocrinology, and machine learning for medical time-series. Cutting-Edge
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barriers: a large input modality gap, as network data consists of diverse, non-textual formats like time-series metrics, graphs, and scalar values; the inefficiency and unreliability of answer generation
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for medical time-series. Cutting-Edge Resources: Benefit from state-of-the-art High-Performance Computing (HPC) facilities, unique multimodal datasets, and advanced wearable technologies. Global Collaboration
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of ENDOTRAIN and will advance the development of multimodal Large Language Models (LLMs) for integrating time-series physiological data, clinical assessments, and natural language summaries in the context
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part of Work Package 2 – Technologies for Multimodal data of ENDOTRAIN and will advance the development of multimodal Large Language Models (LLMs) for integrating time-series physiological data, clinical
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for time-series/wearables data, demonstrated by relevant courses and master thesis. Having publications in the area is highly valued as well demonstrated ability to work both independently and as a team
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37 of the Statutes of Universidade do Minho, approved by Normative Order no. 15/2021, published in Diário da República, 2nd series, no. 115, of June 16th, makes it known that, for a period of 10
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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collected samples from Skåne, the PhD student will help reconstruct biodiversity time series and assess how forest management impacts species dynamics. The position will involve lab and field work