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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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of functional membrane proteins embedded in a micelle or lipid (nanodisc) environment Site-directed mutagenesis for site-specific incorporation of spectroscopic probes (e.g. spin labels or fluorescent labels
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techniques. Preferred Qualifications: Knowledge of HPC matrix, tensor and graph algorithms. Knowledge of GPU CUDA and HIP programming Knowledge on distributed algorithms using MPI and other frameworks such as
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interactions Perform in vitro transcription, RNA purification, labeling, and structural probing Analyze experimental data, interpret results, and contribute to manuscript preparation Present research findings
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methods (e.g., fluorescence, reflectance, or label-free modalities). Collaborate on development and optimization of imaging instrumentation and workflows. Perform tissue preparation, imaging, and data
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include expressing recombinant proteins in insect, mammalian, and E. coli cells; purifying proteins using FPLC systems; performing in vitro RNA transcription and labeling; carrying out in vitro assays
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closure modeling and/or high performance computing environments (MPI, CUDA) • Expertise in software development and computing tools (C/C++, python, git, parallel computing, etc.) • Experience with deep
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physics knowledge into DL model design and training, these models outperform traditional methods even without labeled training data (https://www.nature.com/articles/s41524-022-00803-w ). Application spaces
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microbiomes. Within this focus, the research group has a strong program in the synthesis and application of chemical probes in complex biological systems and measurements of probe labeling by LC-MS based
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-scale multi-area in vivo neural recording, wireless electrophysiology in freely moving animals, custom-engineered sensory-motor behaviors, neural perturbation, viral labeling, and computation. Our work is