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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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, executes and reviews sponsored software research project on structural design and optimization, evolutionary and ML methods; works closely and interacts with industry partners to scope out projects and
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MaTRIX Lab works closely with experimental scientists and clinicians to identify novel biomarkers, optimize therapeutic strategies, and improve disease diagnosis and prognosis. By bridging mathematical
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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the current state of knowledge on specific drugs’ PK and PD properties and on state-of-the-art approaches for study design optimization, dose individualization, and PK/PD modeling and simulation. Frequent
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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, qPCR, and cloning. Troubleshooting: Optimize molecular biology assays like western blotting and qPCR. Documentation: All team members are expected to meticulously document experiments, data, and analyses
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requirements for specific experimental goals. Experimental design and optimization with the laboratory Director to advise investigators regarding experimental design and data analysis. Work with investigators
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, chemists, and clinicians. This individual will be responsible for the development, optimization, testing, and implementation of a wide variety of proteomics sample preparation and data acquisition protocols