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Field
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The Distinguished Research Fellowship Program seeks PhD graduates from underrepresented groups for postdoctoral experience and training in the School of Engineering and Applied Sciences . The aim of the program is to
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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Essential Qualifications and Experience: PhD (or near completion) in political science, social sciences, public/global health, or related field Strong understanding of climate change and health, particularly
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-scale assessment data, meta-analyses of meta-analyses) Methods and approaches to cumulative, living, and community-augmented meta-analyses Methods and approaches to include machine learning and artificial
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for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings
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training materials for research teams, focusing on data science and machine learning techniques in geoscience. Position description: PD [Research Fellow] [520112].pdf To learn more about this opportunity
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will possess a relevant PhD or equivalent qualification/experience in a relevant field of study (e.g. data science, AI, machine learning, statistics, physics). They will be motivated to solve problems
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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element method, wave propagation analysis, inverse problem, machine learning (ML), and artificial neural network. Strong background in research publications in the desired field. Experience mentoring other
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experience in IC design/Electronics Familiar with Analog/Power Management/RF/ADC/DAC IC design Proficiency in Cadence Proficiency in Python and Matlab Experience in implementing algorithms for machine learning