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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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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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exposome and dynamic exposome modeling, learning in timeseries and spatial data, and hybrid deep learning-causal modeling. The successful applicant should have significant research experience in at least two
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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
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afraid of combining neurobiology and chemistry. You have good statistical skills and experience with analyzing big data (e.g. RNA-seq, spatial transcriptomics). You like to work in a diverse setting and
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of the Earth system at different temporal and spatial scales to improve predictive capability. Comprehensive education: Enjoy numerous opportunities for scientific training, skills development and problem
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grasp of spatial statistical methods in R is considered highly advantageous. Excellent communication skills are required, with proficiency in English and preferably one of the Scandinavian languages
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, stress or endocannabinoid system. You like a challenge and are not afraid of combining neurobiology and chemistry. You have good statistical skills and experience with analyzing big data (e.g. RNA-seq
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changing spatial regulations. You will also help design economic decision-support tools to inform more inclusive and evidence-based marine policy. Your duties and responsibilities include: analyzing