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economics and finance. Leveraging its ‘4-in-1’ model of education and residential college system, UM provides all-round undergraduate education, nurturing talent to support social and economic development in
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highly motivated PhD student to develop advanced fracture models for predicting material degradation and failure in additively manufactured steel in nuclear reactor water environments. The project focuses
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are developing methods and tools to explore and understand immunity across domains of life, from genomics to experimental work in various model organisms. The role of the engineer will be to consolidate, update
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on developing a new multi-disorder prediction approach that integrates different sources of information. You work with analytical model development, extensive simulation studies and analysis of existing large
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are analysed using big data and business intelligence applications to monitor tourisms. Additionally, predictive modeling methods are applied to estimate tourist mobility behavior and movement patterns between
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 7 days ago
intelligence is growing at a fast pace, the bulk of the world's computing power remains targeted at modeling and predicting physical phenomena, such as climate models, weather forecasting, or nuclear physics
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intelligent surfaces Main supervisor: Prof. Viktar Asadchy[AALTO] Co-supervisors/mentors: Dr. Victoria Tormo [INDRA] and Dr. Barthès [3DEUS] Objectives To establish an analytical modeling approach
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 24 days ago
learning models for antimicrobial activity prediction (e.g., Weka); - Strong communication skills; - Fluency in English (written and spoken). The candidate must demonstrate interest in microbiology and
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods