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. This limits our ability to predictably control and exploit the drop for useful tasks. Aims: 1. Develop computational models to quantitatively predict the response of chemically active drops to the
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, sustainability, and advanced process control, aiming to develop AI-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and
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, reliability, and autonomy Design and implement advanced control strategies (e.g., model predictive control, adaptive control, feedback/feedforward control, ML-based controls) for thermal system
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technologies, predictive modelling and digital control systems create new opportunities to address this problem. By combining real-time measurements of fruit and its environment with models capable of
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health data science. Primary responsibilities Design, implement, and validate predictive models that use imaging data (e.g., CT, MRI, PET) to forecast response to adjuvant treatments. Build end-to
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-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g., compressors, reaction kettles). Research advanced process control strategies and deploy
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-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g., compressors, reaction kettles). Research advanced process control strategies and deploy
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500 prototypes through multi-omics, sensory analysis, life-cycle assessment and safety testing. Predictive models for flavor, texture, nutrition and process scale-up are trained iteratively through
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datasets to develop predictive models of therapy response and identify clinically actionable biomarkers. Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life
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clinical research with the primary aim of improving health services for Veterans, particularly in the areas of blood pressure control, acute and chronic kidney disease, antibiotic stewardship, and