126 parallel-processing-bioinformatics positions at California Institute of Technology in United States
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analysis and processing in the lab. Work with lab members to understand needed bioinformatic capabilities and develop tools to accomplish them. Work with lab members and external collaborators to help them
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interfaces with observatory facilities and instrumentation, including stray light control, contamination control, mechanical vibration, manufacturing, logistics, assembly and test processes, and cleanroom
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numbers of samples in parallel. Prior experience handling and processing 96-sample assays, such as 96-well PCR plates or 96-well screening assays. Interest in liquid-handler automation of wet-lab workflows
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engineering to bioinformatics and the nature of life itself, from human behavior and economics to energy and sustainability. Caltech is small but prizes excellence and ambition. The contributions of Caltech's
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to design and implement data models and a database for genomic data integration. (30%) Develop and maintain an open-source bioinformatics platform as part of the Alliance of Genome Resources initiative
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engineering to bioinformatics and the nature of life itself, from human behavior and economics to energy and sustainability. Caltech is small but prizes excellence and ambition. The contributions of Caltech's
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to the mouse and cryo-electron miscroscopy projects in the Chen lab at Caltech. Essential Job Duties Manage mouse colonies for the lab and receive training for basic surgery operations (e.g., removing specific
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-of-the-art algorithms for detecting and classifying safe landing zones using computer vision techniques in order to design and develop custom models for detecting landing zones in real-time based on drone
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calls, managing visitors, welcoming and directing guests, and reviewing and routing mail. Provide administrative assistance to multiple attorneys requiring a thorough knowledge of OGC’s processes and the
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part of the diverse Caltech community. Job Summary This position involves research and evaluation of state-of-the-art algorithms for detecting and classifying safe landing zones using computer vision