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Field
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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-year research project aimed at advancing our understanding of foot growth and deformities in children with cerebral palsy. In this project, you will develop statistical shape models and finite element
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, Machine Learning, or Smart Energy Publication record in peer-reviewed journals or conferences, commensurate with stage of career Good programming skills in Python, R, Java, or Matlab Experience
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, machine learning and Quantitative Microbial Risk Assessment Microsimulation modelling, generating evidence directly relevant to national food safety policy. You will join a PhD student cohort with NIHR
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laboratories. The topic of the doctoral research will on development of efficient and scalable search engines. This entails design of new retrieval models, efficient query processing, and use of modern hardware
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feedback modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. Description of the SPARK4B+ project The position is within the EU
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with a background in theoretical physics, statistical, soft or biological physics Experience with using inference/machine learning tools and basic programming is a plus As a university, we strive for
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problem is computationally intractable. This project uses an AI-guided inverse design loop. A goal-directed AI proposes novel GNR architectures, which a fast machine learning model rapidly evaluates
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. The research may involve a combination of commercial process simulation tools and in-house model development for individual unit operations and integrated systems. Hybrid modeling approaches combining first
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PhD candidate you will develop new ways to extract cosmic-ray physics from KM3NeT data. You will design and characterise reconstruction methods—both machine-learning-based and traditional