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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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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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, Shiny, Plotly). Applications: Experience with biomarkers and epidemiological studies: survival analysis, longitudinal modeling, multivariate analysis, and Bayesian statistics. Experience with statistical
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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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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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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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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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command of English in both speech and writing, and ability to communicate in a professional, objective and transparent way with personnel and users. Other specific qualities and qualifications of merit