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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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of Medical Biosciences, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://erdemlab.github.io . The Erdem research
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highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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from Hi-C and Capture Hi-C experiments. Have experience developing graphical user interfaces (GUIs). Candidates with knowledge or experience in machine learning methods will be prioritized. Successful
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Biosciences, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://erdemlab.github.io . The Erdem research group is
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data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft