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phenotyping, microbiome profiling, predictive modelling and targeted experimental validation to identify the microbial factors that make plants more resilient to disease. Your job You will join the Plant
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phase-field models require the underlying free-energy density as an input. This internship aims to compute that free-energy landscape from first principles and transfer it to continuum electrode models
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-quality agents, predictive models that can transfer across machine families, prescriptive decision support, operational dashboards, and ensuring the delivery of methodologies, documentation, training, and
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the statistical analysis of large, multidimensional agricultural, genomic or environmental datasets. Experience with mixed models, genomic prediction, crop simulation, machine learning, hierarchical modelling
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Kong Chian School of Medicine. The role will focus on analysing genomic and clinical data and supporting the development of cancer survival prediction models. Key Responsibilities: • Analyse and
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technologies used to generate, harvest and store clinical data and methods used to create predictive models (including but not limited to methods associated with machine learning). Furthermore, issues related
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maintenance of command-line and web-based applications deploying machine learning (ML) models for chemical synthesis planning, property prediction, and molecular design and will work closely with faculty
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resilience, pathogen adaptation, quantitative disease biology, predictive modelling, or mechanistic understanding of plant-pathogen interactions. As an Associate Professor in the Laboratory of Phytopathology
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Science (DIHDS) and the Media and Innovation Laboratory (The MIL). The Manager applies advanced analytics, artificial intelligence, machine learning, predictive modeling, and related data-driven
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to grouted BHEs, ii) the influence of minewater circulation on thermal efficiency and heat plume evolution; iii) predicting flow pathways in a real-world case study emulating a system with high throughflow