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single-cell atlases with spatial transcriptomics to understand signaling pathways and gene-regulatory dynamics. Explainable AI (XAI): Ensure models provide mechanistic insights into cancer cell states
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test a conceptual SDI model for integrating heterogeneous (conventional geospatial, environmental, behavioural, and human-centred) datasets, including qualitative and quantitative data, across spatial
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change. Experience in quantitative methods, spatial analysis, or handling large datasets would be valuable, but full training will be provided in climate modelling, statistical downscaling, and health
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other post-doctoral researchers. The project will also provide a specific budget for a laptop and for travels to workshops and other meetings. REQUIREMENTS: A PhD in an environmental, economic, or related
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. • Design and implement studies on themes such as recreational quality, mental well-being, social connectivity, and resilience—integrating geospatial and spatial-temporal analysis to assess patterns
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models or glioblastoma research Familiarity with transcriptomic methods (RNA-seq, FISH, spatial transcriptomics) Programming skills for data analysis (Python, R, or MATLAB) We offer Funding: Full position
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/resources/industrial-iot-lab ) and residential environments (https://idlab.ugent.be/resources/homelab ). Experimental validation can be backed by modelling or theoretical assessments. You collaborate with
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undergraduate students in independent research projects through a summer program for incoming transfer students. Find out more about the lab at https://coevolving.org . REQUIRED QUALIFICATIONS: o A PhD
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., Pettersson, H., Behrens, A., Männik A., 2018. Comparing a 41-year model hindcast with decades of wave measurements from the Baltic Sea. Ocean Engineering, 152, 57–71. https://doi.org/10.1016/j.oceaneng
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, the candidate will work in an interdisciplinary team of biologists, statisticians and philosophers, including one other PhD student (statistics) and two postdocs (spatial forest ecology and philosophy/social