We seek a highly motivated biostatistician or computational biologist who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific programming for projects on neurological and cancer diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell omics and clinical data to predict clinical outcomes of interest and therapeutics. This will include implementing and applying software analysis pipelines and interpreting disease-related data together with experimental and clinical collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease- and treatment-associated alterations in complex neurological and cancer disorders
Requirements:
The candidate will have an MSc or equivalent degree in biostatistics, bioinformatics, computational biology, machine learning, or related subject areas; Prior experience in large-scale data processing and statistics/machine learning is required; Previous work and publications in bioinformatics analysis of large-scale biomedical data (e.g., omics, clinical, structural bioinformatics, other biomedical data) should be outlined in the CV; Demonstrated skills and knowledge in omics data analysis, biostatistics, machine learning, pathway and network analysis are highly advantageous; Experience with analysis of epigenetic data (e.g. ATAC-seq, ChIP-seq) is a plus
Text:
PhD in Biostatistics, Computational Biology and Bioinformatics We seek a highly motivated biostatistician or computational biologist who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific programming for projects on neurological and cancer diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell omics and clinical data to predict clinical outcomes of interest and therapeutics. This will include implementing and applying software analysis pipelines and interpreting disease-related data together with experimental and clinical collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease- and treatment-associated alterations in complex neurological and cancer disorders The candidate will have an MSc or equivalent degree in biostatistics, bioinformatics, computational biology, machine learning, or related subject areas; Prior experience in large-scale data processing and statistics/machine learning is required; Previous work and publications in bioinformatics analysis of large-scale biomedical data (e.g., omics, clinical, structural bioinformatics, other biomedical data) should be outlined in the CV; Demonstrated skills and knowledge in omics data analysis, biostatistics, machine learning, pathway and network analysis are highly advantageous; Experience with analysis of epigenetic data (e.g. ATAC-seq, ChIP-seq) is a plus
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