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have: Ph.D. in Biostatistics, Statistics, Bioinformatics, Data Science, or related field; with (i). technical skills such as Proficiency in R, Python, or SAS for statistical analysis and modelling; (ii
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. quantitative and/or qualitative counterfactual-based approaches, Difference in Difference models, Qualitative Comparative Analysis, Bayesian hierarchical modelling); o Experience working with and synthesizing
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& AI hardware or brain-inspired AI algorithm development, spatial analysis of multi-omics data. We are particularly interested in applicants with a demonstrated track record of translating discoveries
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) Development of state-of-the-art database and analysis platforms as part of data science, utilizing diverse datasets related to earthquakes and volcanoes across broad spatial and temporal scales. These datasets
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to the Institute’s needs. Requirements for candidates: Proven experience in advanced hydrological modeling (e.g., conceptual or physically based spatially distributed models), particularly with SWAT+, SWAT-MODFLOW and
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, and spatial), RNAseq (bulk, single-cell, and spatial), and other multi-omic approaches in collaboration with the appropriate institutional core facilities. In addition, cultures mammalian cell lines
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methods for forensics, life sciences, topological data analysis, spatial statistics, and computational statistics). The department values an informal atmosphere with strong collegial support, openness, and
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translate into concrete health outcomes. The BRANCH project wants to change that. By combining conceptual work on green space typologies, advanced geospatial analytics, AI-based image analysis, citizen
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. Your team You will collaborate with GRS colleagues who have expertise in methods and tools for spatiotemporal analysis of complex land systems (including agent-based modelling), spatial data
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aims at developing a unified, scalable, and interpretable framework for tensor analysis. Specifically, the project will: Develop novel, modular statistical solvers to integrate domain-specific knowledge