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Field
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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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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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-atomic potentials using a combination of classical and machine-learning (ML) approaches (and a new hybrid method recently developed in our group). Some of the types of simulations that will be performed
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machine learning frameworks. Proficiency in writing clean, efficient, and well-documented code. Mathematics skills including linear algebra and partial differential equations. Ability to implement software
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, sensor networks and measurement technology, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will be part of a research
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modeling. These projects will focus on how to leverage ideas from a combination of partial differential equation based computational methodologies and machine learning techniques to develop computational
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
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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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project/work tasks: The SnowAI project aims to use to produce new high-resolution datasets on snow depth in Western Norway derived from machine learning and radar remote sensing. The successful PhD
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field of engineering Research experience in one or more of the following: geometry, optimization, dynamical systems, mechanics, probability and statistics, data science, machine learning Evidence of