We seek a highly motivated bioinformatician or biostatistician who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific programming for a project on the study of neurodegenerative diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical, imaging, or other large biological datasets), using statistical methods, pathway/network analysis or machine learning/artificial intelligence. The candidate will conduct integrative analyses of biomedical datasets, focusing on omics and clinical data to predict clinical outcomes of interest (e.g., disease progression scores). This will include implementing and applying software analysis pipelines and interpreting disease-related data together with experimental and clinical collaborators. Classification models guided by prior mechanistic knowledge will be built, exploiting the known grouping structures among features in the omics data, using dedicated structured learning algorithms. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease-associated alterations in neurological disorders
Requirements:
The candidate will have an MSc or equivalent degree in bioinformatics, biostatistics, machine learning, computational biology, or related subject areas; Prior experience in large-scale data processing and statistics/machine learning is required; Previous work and publications in bioinformatics or biostatistics analysis of large-scale biomedical data (e.g., omics, clinical, imaging, other biomedical data) should be outlined in the CV; Demonstrated skills and knowledge in omics data analysis, machine learning, pathway and network analysis are highly advantageous; The candidate should have a cross-disciplinary aptitude, strong organizational and interpersonal skills, and a keen interest in collaborative biomedical research; Fluency in oral and written English
Text:
PhD in Bioinformatics, Biostatistics and Artificial Intelligence We seek a highly motivated bioinformatician or biostatistician who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific programming for a project on the study of neurodegenerative diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical, imaging, or other large biological datasets), using statistical methods, pathway/network analysis or machine learning/artificial intelligence. The candidate will conduct integrative analyses of biomedical datasets, focusing on omics and clinical data to predict clinical outcomes of interest (e.g., disease progression scores). This will include implementing and applying software analysis pipelines and interpreting disease-related data together with experimental and clinical collaborators. Classification models guided by prior mechanistic knowledge will be built, exploiting the known grouping structures among features in the omics data, using dedicated structured learning algorithms. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease-associated alterations in neurological disorders The candidate will have an MSc or equivalent degree in bioinformatics, biostatistics, machine learning, computational biology, or related subject areas; Prior experience in large-scale data processing and statistics/machine learning is required; Previous work and publications in bioinformatics or biostatistics analysis of large-scale biomedical data (e.g., omics, clinical, imaging, other biomedical data) should be outlined in the CV; Demonstrated skills and knowledge in omics data analysis, machine learning, pathway and network analysis are highly advantageous; The candidate should have a cross-disciplinary aptitude, strong organizational and interpersonal skills, and a keen interest in collaborative biomedical research; Fluency in oral and written English
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