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to identify metabolic response patterns and develop predictive models for personalized nutrition. Supervising master’s and/or doctoral students to a certain extent Possibility to engage in teaching at
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predictive confidence, including sensitivity and identifiability analyses Compare grey-box models against purely mechanistic and purely data-driven approaches Optimize model performance for computational
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Modeling Center (EMC) and Climate Prediction Center scientists in the design of numerical experiments. Required Qualifications: Terminal degree in a related field or the equivalent combination of education
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: Implementation and fine-tuning of antibody design models (RFdiffusion and boltzgen, AlphaFold3 etc.). Implementation of affinity prediction and maturation (FoldX, RosettaFold, ESM etc.), virtual screening and
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posterior predictive checks, model comparison, programming in R (python/Matlab), implementations using R-packages rstan/JAGS and brms/STAN or equivalent interfaces. References Lages, M. A hierarchical signal
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large datasets in wheat, Develop and implement novel approaches for genome-wide predictions of complex traits. Your qualifications and skills: You hold a MSc in plant science, plant breeding, biology, or
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will use a large dataset of P. aeruginosa genomes and experimental metadata to predict key mutations to the organism. The postdoctoral researcher will join the Whelan lab led by Dr. Fiona Whelan
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their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? We are looking for a recognised business development
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models. Misclassification Characterization (Month 4-5) Construct an augmented misclassification dataset containing: original and perturbed variants, model predictions, correct labels, perturbation type
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to predict thermal runaway on the cell level. The combustion and gas model developed on the cell level will then feed into the work to accurately predict thermal runaway on pack, module, and system levels