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molecular network analyses, master regulator analysis, and multi-modal data integration approaches. § Maintenance of datasets and FAIR data compliance, including lodging data on open access sites
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limited to art-based interventions, mindfulness-meditation, motivational interviewing, meta-cognitive coaching, and multi-modal interventions). The applicants should have the relevant methodological
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emphasis on Smart-seq3xpress and spatial transcriptomics methods including MERSCOPE, and TRISCO platforms. A central component of the project will be the computational analysis and integration of single
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with histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI
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through peer-reviewed publications. Programming experience in Python or similar environments for data analysis, visualization and/or automation. Innovative experimental skills with experience in
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interventions, and brain stimulation reposition the brain within a shared functional space, revealing principles for prediction and control. We combine multi-modal neuroimaging and consciousness science with
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modelling, machine learning, network analysis, and systems biology approaches to large-scale datasets. Perform longitudinal and multi-modal data integration to investigate disease trajectories and
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these areas is a strong advantage: Experience with neural tissue culture and/or iPSC differentiation Experience with 3D in vitro culture and Organ-on-Chip fabrication Experience with image analysis and/or
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SciLifeLab ( https://www.scilifelab.se/researchers/kimmo-kartasalo/ ). We work closely together with other groups at MEB within and beyond the department’s prostate cancer research environment, as
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expertise14) together with deep learning (for efficient representation and cross-modal learning) to discover the potential therapeutic landscape for novel therapies in breast cancer, and to propose