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, Bioengineering, or a closely related quantitative field in hand by the start of the appointment. ● Strong background in one or more of: stochastic processes, probability theory, dynamical systems, statistical
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, or reproducible research workflows. Knowledge of one or more of the following: GIS, remote sensing, computer vision, image processing, machine learning, spatial statistics, or map-based data fusion. Experience
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to ecological or biological questions. Demonstrated experience developing and applying species distribution models, ecological niche models, or related spatial/statistical models. Experience working with large
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Instituto de Geografia e Ordenamento do Território da Universidade de Lisboa | Portugal | 5 days ago
Europe Programme. The successful candidate will support research activities related to geographic information systems (GIS), territorial indicators, open science, public policy analysis, statistical data
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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. Experience with demographic modelling, ecological databases, functional traits, spatial/environmental data or field ecology is desirable. Qualifications A first degree in ecology, biology, environmental
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equation models for Epstein–Barr Virus (EBV) and HIV-1 infection dynamics in human lymphoid tissue. New mathematical models will be informed by longitudinal experimental data, including multiplexed spatial
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(GeoAI). Experience with industry-relevant software and platforms, such as ArcGIS, QGIS, Python, R Statistics, Google Earth Engine, ENVI, or others. Experience teaching GIS, spatial analysis, remote
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for: Analysis of bulk and single cell RNA-sequencing, spatial transcriptomics, and proteomics datasets Integration of experimental model data with public and clinical datasets Statistical modeling and survival
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-seq, and spatial transcriptomics, to investigate gene expression and cellular dynamics in lung development and disease. This role involves close collaboration with bench scientists to design experiments