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Your Job: Chromatography modeling, while crucial for modern bipporcess development, still heavily relies on empirical determination of key model parameters. By combining protein structure
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t image reconstruction methods in a cell microscopy setting using image sequences as well as focus...
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Chromatography modeling, while crucial for modern bipporcess development, still
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19 Jan 2026 Job Information Organisation/Company Forschungszentrum Jülich Research Field All Researcher Profile First Stage Researcher (R1) Application Deadline 19 Jan 2038 - 03:14 (UTC) Country Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research...
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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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for a highly motivated PhD candidate to join our world-leading research program in Earth System modelling and improving Earth System Modeling by better merging of measurement data and model simulations
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modelling and improving Earth System Modeling by better merging of measurement data and model simulations. This PhD project focuses on improving how we estimate key parameters in land-surface and ecosystem
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models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model