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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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. Specifically, NIR sensors, hyperspectral imaging coupled with standard or macro lens, spatially resolved spectrometry, evolving plots, and FTIR will be used for the non-invasive characterisation of raw material
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the development and adaptation of statistical models for analyzing the relationship between species distributions and climate Collaborate closely with other researchers, including other PhD students in connected
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-Spain’s Recovery, Transformation and Resilience Plan 1. First project in which the successful applicant will collaborate: 1.1. Name of the project: “Impact of the spatial-temporal aggregation
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samples lacking cellular and spatial resolution. The hypothesis underpinning this project is that obesity alters the cellular content, activation state and, critically, the spatial architecture
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Environmental behaviour o Spatial analysis applied to territorial/regional planning Technical and soft skills · Basic computer skills (Word, Excel etc.) · Proficiency in statistical analysis
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outcomes of this project will help to better retrieve the soil moisture in the presence of vegetation at high spatial resolution and, at the same time, estimate the plant water content. Automatic and
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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application-oriented research that unlocks the potential of data through rigorous analysis – advancing solutions in societally relevant domains. Your profile Master’s degree in Statistics, Industrial