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Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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-based, Bayesian or matrix factorization methods for multi-omics integration. Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
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, or network-based, Bayesian or matrix factorization methods for multi-omics integration Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
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in both discovery-scale and targeted proteomics approaches. You will work alongside a team of experienced scientists and have access to world-class infrastructure to advance proteogenomics research and
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, volcano, heatmap, and correlation plots, either software-or script-based), and metadata integration. Demonstrated expertise in both untargeted and targeted metabolomics workflows and strong understanding
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emerging anti-cancer therapies, particularly towards targeted immunotherapies. The bioinformatician is also expected to develop tools to facilitate the translation of the work into new clinical initiatives
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immunofluorescence/immunohistochemical staining and flow cytometry. Knowledge of immuno-oncology, with a particular focus on myeloid cells and how targeted therapy can increase antigen uptake and T cell activation
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as a starting point for targeted cellular therapies such as Chimeric Antigen Receptor (CAR) T-cells. You are driven by the need for research advances to reach the patient and therefore have ideas
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and similar substances interact with biological systems. By investigating how such substances affect molecular targets and induce changes in cellular and physiological processes or pathological