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statistical genetics techniques to analyse large-scale single-cell and genomic data. Data Analysis: Process, analyse, and interpret high-throughput single-cell and genomic data to derive biologically meaningful
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the information encoded in our genome to better diagnose, treat, predict and prevent disease. From the individual patient with rare disease, to the many thousands affected by complex, widespread illness, we
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Postdoc position to support international research and capacity-building projects employing elect...
of large-scale EM data for groundwater mapping. Teaching and training of Ethiopian partners and students in EM methods, data processing workflows, inversion software, and geological interpretation
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) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive inverse problems. The team aims at developing
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increasing independence over time. Collaborate on project and analysis design guided by their PI. Develop new computational methods. Adhere to field and lab standards for data analysis. Identify, process
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opportunities to work with large-scale whole genome and whole exome sequencing, RNA-seq, ATAC-seq, proteomics, and metabolomics data in a well-established cohort of childhood cancer survivors with clinically
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addition to the large base of basic research, the center has a large number of ongoing industrial projects and partnerships. In the Valero lab, we offer a dynamic, social and interdisciplinary scientific environment with
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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, handling, and synthesising big data geospatial data sets from various data sources. Cutting-edge expertise in advanced statistical analyses of large data sets and strong knowledge of programming languages
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, Molecular Medicine or a related discipline a high interest in immunological research questions profound experience with Drosophila work experience with large biological datasets and omics data, ideally (scRNA