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sensors will be designed, modeled, fabricated, and fully packaged for deployment. Sensor performance will be measured in the lab and new techniques will be developed to characterize performance for inertial
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to remove PFAS. To accomplish these goals, the candidate will participate in the development of AI/ML algorithms for the prediction of chemical properties, infrared and mass spectra, and ionization cross
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sensors or inductive sensors, and demonstrate temperature readout of magnetic nano-objects engineered with high thermosensitivity. For the past 6 years, our team has been developing instrumentation
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-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements. § Implementing algorithms
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technologies, ethical implications, and governance frameworks, including knowledge of algorithmic accountability and transparency. Experience with both qualitative and quantitative research methods, and
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Experience: Knowledge of AI frameworks and algorithms, particularly those related to decision-making and ethical AI. Machine Learning Experience: Knowledge of ML techniques, including reinforcement learning