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- University of Texas at Austin
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Field
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malicious behaviors implemented in Smali code. Adversarial attack development: you will design and implement adversarial attacks by manipulating localized malicious payloads against malware detection models
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methodologies, including the manipulation and processing of satellite imagery and data from equivalent remote-sensing platforms (e.g., UAVs), as well as the development of machine-learning approaches to enhance
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subjects: microbiology, biochemistry, genetics, bioinformatics Practical experience in the handling and genetic manipulation of microbes as well as DNA-, RNA-, and protein-based techniques is desired
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host immune responses and bacterial physiology. Responsibilities include manipulating the host response to infection, conducting bacterial genetics, performing in vivo mouse work, tissue culture
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combination of high-end microscopy combined with fluorescent biosensor imaging, optogenetics and genetically-encoded molecular tools enabling subcellular manipulation in vitro and in vivo. She/He will also work
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. Preferred Qualifications Hands-on experience in analyzing and manipulating seismological/geophysical data. Proficiency with specific background and hands-on experience with deformation assessment and
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migration of interneurons during corticogenesis. The candidate should preferentially have experience in the following experimental techniques: in utero and in vitro electroporations; manipulation, production
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of DNA/RNA manipulation techniques and design of expression constructs. • General knowledge of in vitro antiviral validation methods (assays on human cell lines). • Understanding of LNP-based approaches
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to manipulate microscale objects, cells and nanoparticles. Based on acoustofluidic principles we develop medical applications such as isolation of circulating tumorcells from cancer patient derived blood samples
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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions