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the year to assist scholarship administrators in monitoring available funding and fund utilization. Prepare scholarship-related reports, student data analyses, and statistical information as requested
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science. Electronic structure calculations (e.g. DFT or tight-binding methods), or thermodynamic modelling (e.g. statistical mechanics, MD or CALPHAD). Scientific data analysis. Interdisciplinary research
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at NTNU. We focus on long-term basic research and applied research at a high international level. Our aim is to meet the society’s needs for mathematical and statistical expertise in business and public
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Mathematics and Mathematical Statistics is opening a fully funded PhD position in mathematics
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observables, including: the spatial distribution and migration of seismicity, magnitude-frequency statistics and temporal clustering, source properties (strain drop, duration, moment-duration scaling), and
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recruiting participants to the design and implementation of a new two-wave cross-national survey); You will use advanced statistical methods to process and analyze data; You will write international peer
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communication skills in English Skills in data management and statistical analysis Other skills and qualifications considered meriting: Experience in scientific writing Written and spoken communication skills in Swedish
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processing and statistical analysis. The PhD thesis will be supervised by two supervisors, Corentin Louis (Chargé de Recherche CNRS, Observatoire de Paris) and Baptiste Cecconi (Astronome, Observatoire de
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to accelerate formulation discovery. Experimental data will be organised into a comprehensive database and analysed using statistical learning and Bayesian optimisation, establishing a closed-loop framework
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
approach is based on neural techniques known as SBI (Simulation-Based Inference) [Cranmer et al., 2020]. SBI enables the resolution of inverse problems using generative AI methods and Bayesian statistics