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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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. Lab Research: • AI-Driven Algorithms & Software: Develop deep leering/machine learning/statistical based algorithms to elucidate lncRNAs, fusion transcripts, RNA modifications, and circular RNAs in
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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bioinformatics analysis pipelines for processing RNA-seq, single-cell RNA-seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when existing software packages
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and atomic experimental physics. Our lab focuses on developing scalable quantum processors using neutral atom arrays, with a strong emphasis on bridging theoretical innovation and experimental
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that can be integrated into the research workflows used in developing new materials (e.g., carbon nanotubes) or in determining disease pathologies (e.g., Alzheimer’s disease). We want to explore solutions
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algorithms to improve methods for peptide identification from raw mass spectral data. The use of orthogonal information such as multi-enzyme digestions, to verify the presence of a peptide using different
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collaborative research projects, develop novel computational algorithms, and contribute to high-quality publication and grant-related activities. The position offers opportunities to work in a dynamic research
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to RNA-seq, except that these are restricted to RNA sequences that have been translated. The goal is to develop computational algorithms and tools to analyse such sequencing data, to predict translated
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developers who work together to solve problems in the following areas: Counter AI Research: Study threat models targeting AI and ML algorithms, understand the behaviors of AI algorithms, identify weak points