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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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-precision vertexing and tracking of charged particles. Past and current projects include the upgrades of the Inner Tracking System (ITS) for the ALICE experiment at the Large Hadron Collider (LHC) at CERN
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currents form the deepest canyons, longest channels and largest sediment accumulations on our planet. They also break seafloor telecommunication cable networks that form the backbone of global data transfer
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available within the Department of Medicine, Division of Endocrinology in the Laboratory of Brain Behavior and Big Data at the University of Southern California. The applicant with work with PI and
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-virus/co-morbid conditions. The incumbent will perform experiments, analyze data, write scientific manuscripts, and present in scientific meetings. Hands on experience in small and large animal handling
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competitive selection (SELEX), starting from artificial libraries of large numbers (~10^15) of distinct sequences. When the experiment succeeds, variants that perform the selected function well are enriched
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to decipher the regulatory code controlling gene expression in plants through the engineering and large-scale validation of synthetic promoters. Within the ERC-funded multiCODE project, we aim
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. Integrate deep mutational scanning data to assess viral fitness and immune escape, collaborating with experimental virologists. Work with large-scale genomic and proteomic datasets from BV-BRC, GISAID, and
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approaches when working with families, professionals and teachers to best support neurodivergent children and young people in different settings. You will be responsible for data collection using quantitative