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integration of genomics, proteomics, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination
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High motivation and enthusiasm for working in an interdisciplinary research environment Research focus: The successful candidate will conduct research on data-driven modeling of transportation systems
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
function of these cell envelopes for several bacterial species. The most powerful models have several parameters set from experimental observations, in this case data gathered through the use of electron
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time (25,025 h/week) PhD position (f/m/d) in Spatial Cancer Biology he Brägelmann Lab at the University Hospital Bonn (UKB), University of Bonn, is seeking a highly motivated PhD candidate to join our
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of thermodynamic modelling, process simulation is an advantage. For applicants to the PhD position, you must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent
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and curate LC-MS/MS data for high-quality feature extraction Design and train machine-learning models for mass spectrometry and chemometric data Integrate multi-omic data including genomics and
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challenges holding back an even wider usage. This PhD position is part of the national NanoMedNL consortium that aims to counter these challenges and thereby accelerate and support the development of novel
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, engineers and PhD candidates. The PhD candidate is expected to develop an advanced engineering noise prediction model for efficient computation of sound propagation in a range-dependent atmosphere where
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and