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Field
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Join the forefront of groundbreaking research at City of Hope where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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employees, whose jobs are being replaced by digital technologies. The Lab partners with organizations to test the feasibility, scalability, and effectiveness of different approaches to the skills challenge
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performance across different plasma scenarios Collaborate with international partners to integrate AI models into broader fusion research programmes, sharing data, methodologies, and validation strategies Job
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Join the forefront of groundbreaking research at the Beckman Research Institute of City of Hope, where we're changing lives and making a real difference in the fight against cancer, diabetes, and
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of research initiatives at Duke-NUS. Design and develop AI and statistical methods for complex omics data, including multi-modality (e.g. DNA, RNA, protein, PTM, metabolites) in different resolutions (e.g
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digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different conditions. To propose a methodology/framework in a
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statistical software (e.g., R, Stata, SPSS, Python) • Expertise in quantitative analysis, with preferred skills in o Quasi-experimental evaluation techniques (e.g., Difference-in-difference, PSM) o Social
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scenario analyses to examine how noise might evolve across different technological and operational futures and how it interacts with emissions, policy options and airport‑level constraints. This is a key
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prompts Develop predictive models for sepsis, deterioration and ICU outcomes Create reproducible analysis frameworks using R/Python, GitHub, Quarto or R Markdown Produce real-time dashboards for ICU