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Field
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mitigate turbulence under diverse urban layouts and meteorological conditions. To achieve this, the project explores advanced machine learning approaches, including surrogate modeling and reinforcement
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total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities
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. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with
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advanced modelling, data analysis, and algorithmic development. Your tasks will support our core research focus of mathematical and computational approaches to design and implement solution algorithms, with
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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pneumoniae and Acinetobacter baumannii. You will be working in a trans-national research consortium including experts in structure-based modelling, structure biology and chemistry. PhD in Molecular Biology
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genome-wide CRISPR functional screening in both mouse models and clinical samples. Working closely with computational biologists and clinicians, this interdisciplinary project offers an exceptional
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. Familiarity with computational modeling of structure-function relationship Experience in identifying metabolites in biological samples. Sufficient knowledge in statistical analysis tools (e.g., SAS, R, XLstat
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Moustafa to carry out cutting-edge research in the broad area of future structures and resilient infrastructure systems with interest in applications of new and emerging technologies. Particular areas
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collaboration with Growgraph, an R&D startup engaged in advancing knowledge graph technologies and AI-driven methods for structured data analysis. This partnership aims to foster the transfer of research outcomes