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Field
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knowledge of statistical inference, statistical modelling You are able to work in a team while maintaining autonomy Proficiency in English (speaking, writing, reading and listening) You have demonstrated
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to identify which exposures matter most, and which populations and locations are most vulnerable; Combine deep learning with causal inference to distinguish correlation from causal environmental effects; Extend
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, LiDAR ) and minimal experience of five years with SAR remote sensing (Sentinel-1; ALOS-PALSAR and NISAR); - Experience in forest modeling; - Experience in machine learning algorithms, inference
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dataDesigning hierarchical graph‑based models to predict toxicity under uncertainty by linking molecular‑level and system‑level knowledgeAdvancing causal inference methods to predict transformation products under
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low-dimensional systems , Quantum Transport in topological phases , Radiation Therapy Simulation , RF Engineering , Scattering amplitudes , simulation-based inference , single- and multicomponent
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, and which populations and locations are most vulnerable; Combine deep learning with causal inference to distinguish correlation from causal environmental effects; Extend the population-level models
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courses in health economics, causal inference, microeconometrics, health inequalities, medical epidemiology, and healthcare decision-making. • Electives covering data science, public health, implementation
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on training and inference in Edge AI. Familiarity with computer architecture. Strong programming skills. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work
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as event occurrence patterns, mark behaviour, and interactions between events, to vary locally in space and evolve over time. The research will include model development, statistical inference and
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intelligence over the next five years SURE-AI is a Norwegian AI centre funded by the Research Council of Norway (2025-2030). The primary objective is to create a new generation of algorithms for inference and