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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling
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models, spatial Bayesian methods, case time series, case crossover. Have experience with the management and analysis of large climate and/or health databases. Have experience with Linux environment and
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. Candidates with a strong background in the development of novel models and original methodologies are preferred. Requirements: Applicants should possess a relevant PhD degree with a solid and outstanding track
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the Experiment grant. The candidate must have: A PhD degree in a relevant field (e.g. Molecular Biology, Neuroscience). A proven track record either in spatial transcriptomics or extracellular vesicle biology. The
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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Data Science department can be found at https://scds.uoregon.edu/ds. Particular strengths of collaborative research at UO include astronomy, biomedical data science, climate science and modeling, cell
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Austrian Academy of Sciences, The Vienna Institute of Demography (VID) | Austria | about 1 month ago
to join the project “Spatio-temporal modelling of family change” (SPATEMO; funded by FWF ), a French-Austrian collaboration between the VID and IDEES (CNRS) . Using a spatio-temporal, empirical and spatial
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degree. More about being a doctoral student at LTH on lth.se. https://www.lth.se/english/study-at-lth/phd-studies/ Subject and project description The position’s research focus is within the field
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on spatial reasoning, semantic technologies and AI for geosciences. Where to apply Website https://www.academictransfer.com/en/jobs/356999/phd-semantic-modelling-of-geoda… Requirements Specific Requirements A
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, the successful applicant will analyze integral-field spectroscopy (IFS) data of starburst galaxies, with a primary focus on the spatially resolved star formation history and on the physical properties