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journal articles Contribution to the overall support of the project, working group, and team collaboration Requirements: PhD in data science, or in any field with relevant experience Experience with
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theory Competency in processing and analyzing time-resolved data and handling large datasets. Synthesis and characterization of polyoxometalates Electrochemical catalysis and characterization techniques
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for both ligands and novel coordination clusters under a range of conditions. Candidates must also be able to work well independently but also as part of a large team, regularly reporting on progress
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
Integration of phenotypic data with omics analysis Explore machine learning and network analysis methods Profile Essential A PhD in Bioinformatics, Computational Biology, Evolutionary Biology
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | about 1 month ago
using various HPC clusters for large scale simulations of complex nonlinear partial differential equations (PDEs). · Experience of publishing research in high-impact, peer-reviewed journals and present
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uncovering companion biomarkers for precision treatment using cutting-edge bioinformatics and proteomics approaches. You will analyze bulk and clonal protein expression data from large melanoma cohorts
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driven individual with a PhD in data science, computer science, biomedical informatics, or a similar background with some experience working with large datasets. Prior experience with healthcare is not
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-ATLAS cluster, composed of three UAE institutions. Candidates must demonstrate experience in experimental high energy physics. The group will be contributing to the Physics Modeling (MC software, MC
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computing clusters and analysis of transcriptomics and genomics datasets. Desirable Requirements Experience in single-cell and spatial OMICS data analysis. Development of ShinyApps and
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bulk and clonal protein expression data from large melanoma cohorts, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML