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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set
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intersection of machine learning and life sciences, developing next-generation models that improve our understanding of human biology and enable more proactive, personalized healthcare. As an Industrial PhD
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) to help shape and accelerate the adoption of advanced machine learning and AI in data-driven Life Science research. At the SciLifeLab Bioinformatics Platform (NBIS), a unique national infrastructure with
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study design, conduct high-quality omics analyses and statistical and machine-learning based modeling, as well as gaining a deeper understanding in extracellular vesicle biology. Work duties and
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a doctoral student with a strong background in machine learning, mathematics, and modeling, and an interest in biological systems. The successful candidate will join a project to understand and model
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agents Experience developing infrastructure for machine learning workflows Experience contributing to open data platforms or large scientific databases Awareness of diversity and equal opportunity issues
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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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of cohort analysis, prediction modelling, or machine learning techniques • Good knowledge about pancreatic cancer epidemiology • Excel in R or SAS • Good publication records Priority will be given
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features