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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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part of SciLifeLab BioImage Informatics Facility (BIIF) as described below. The Vi3-Division gathers a unique combination of expertise in computerized image processing, human computer interaction, and
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, FDR estimation, etc. Knowledge of statistical and quantitative methods for protein and peptide level analysis, including normalization, missing-value handling, differential quantification, clustering
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bioinformatic methods to detect environmental adaptation. The methods will be tested using simulations of genomic data. The work consists of working in Uppsala University’s computer cluster as well as programming
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: Experience combining proteomics with genomic/transcriptomic data Specialized knowledge: Understanding of peptide-spectrum matching, FDR estimation, protein inference AI/ML proficiency: Experience with machine
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visualization for MS-based proteomics datasets. Knowledge of website building (on Django, Rshiny, etc.) Specialized knowledge in MS-based proteomics: understanding of peptide-spectrum matching, FDR estimation
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developing data processing pipelines, such as Nextflow or similar systems. Computer clusters and distributed systems. Virtual environments, e.g., Docker or Apptainer. Methods for software deployment (e.g