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environments (Slurm, job queue management), use of containers (Docker/Singularity), and reproducible analysis. - Experience in structural modelling and in silico drug discovery, including virtual screening
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verification and Large Language Model (LLM) safety, focusing on extending state-of-the-art logic-based automated reasoning tools such as ESBMC (https://github.com/esbmc/esbmc ) to address safety and reliability
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. This position will direct clinically inspired research programs leveraging a large, well-annotated library of patient-derived cancer models to evaluate novel therapeutics, drug combinations and develop biomarkers
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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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, the Engineer will architect, engineer, and deploy AI pipelines that push technological boundaries for our clients. The Engineer will tackle complex challenges at the intersection of Large Language Models
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safer, more reliable, and more sustainable renewable energy systems. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking for a highly motivated Research Fellow to join our team to lead research in Green Economy (GE) and
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available in the LEAF lab at Boise State University (https://www.leaf-ecohydrology.org/), funded by the USDA Agricultural Research Service. The successful applicant will work with a collaborative team to
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initiation and progression. Using murine and humanized models, human patient samples, single-cell multiomics (CITE-seq), advanced flow cytometry, proteomics, and mechanistic studies, we aim to identify
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. Implementation of programming tasks, data processing, physical installation of sensors, DevOps, and maintenance of physical and logical systems for the modelling of wastewater overflows. Where to apply Website