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: Bayesian hierarchical deconvolution of spatial bins using matched snRNA-seq reference, cell-cell communication inference, and spatial niche identification Multi-omics integration: linking spatial and single
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, clinical research, or translational science. Workflows: Proven track record in developing and implementing workflows to analyze biological data (e.g., proteomics, genomics, transcriptomics or multi-omics
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learning or deep learning, preferably with transformer architectures Experience in probabilistic modelling or Bayesian statistics Programming skills in Python, preferably with PyTorch or similar frameworks
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multi-objective optimization problem, where selection strives to balance the costs and benefits of different traits to optimally position organisms in a high-dimensional trait space. You will explore
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Alice Wallenberg Foundation, with the objective of building globally competitive research environments at the interface of life science, data science, and artificial intelligence. The postdoctoral program
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. You will collaborate closely with the principal investigator, other postdocs, PhD students, and external collaborators to advance research objectives and generate high-impact results. In
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an international, stimulating, and collaborative research environment where your scientific career development is promoted. The project aims to track strain wide differences within human gut bacteria species in
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into how molecular relations shape health and disease in tissues. Yet, analytical tools capable of integrating multi-dimensional spatial data remain limited. The project objectives are to (i) develop tools
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knowledge of relevant machine learning and AI techniques Proven scientific excellence as evidenced by strong scientific publications and track record relative to career stage Strong programming skills (Python
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tracking, and integrating these models into automated analysis and imaging workflows. The work also involves managing and quantitatively analyzing large-scale image datasets, method development in close