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cultures and in vitro tissue modelling B3. Knowledge of single-cell RNA sequencing analysis B4 Knowledge of spatial biology techniques For appointment at grade 7: A4. Normally Scottish Credit and
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include developing foundation models for genome interpretation; creating methods for multi-omic and spatial data analysis and integration with phenotypic and clinical data; and advancing AI-based frameworks
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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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policy, urban planning, data science, environmental science, or a related field. • Strong quantitative skills in applied economic analysis (e.g., causal inference, econometrics, spatial equilibrium
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, and analysis of existing campus and building sites to support long-term campus development goals. Additional duties include mapping and recording site information, as well as documenting and maintaining
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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 Required Qualifications Ph.D. in Bioinformatics, Computational Biology, Systems Biology, or a related field Proven experience in the analysis of single-cell or spatial omics datasets Strong
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through model forests. Granular Matter, 22(1), 1–10. https://doi.org/10.1007/s10035-019-0980-9 Zhang, Y., Shen, C., Zhou, S., & Luo, X. (2022). Analysis of the Influence of Forests on Landslides in
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
), contributing specifically to the tasks done by the IST-ID team. The work will focus on creating a robust design tool that synergistically combines ray-tracing optical analysis with a 1D steady-state thermal
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join our dynamic research team. Our work focuses on craniofacial regeneration and distraction osteogenesis, using cutting-edge multi-omic approaches (single-cell and spatial transcriptomics, genetic and