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of precision fermentation or cell culture - Affinity towards technical tasks and bioprocess control - Advantageous: Experience in mathematic modeling, programming, CAD, microfluidic or bioreactor systems
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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or diploma in physics, biophysics, applied mathematics, chemistry, or a relevant engineering discipline Strong motivation to study motility of microswimmers and develop computational models Good programming
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, their achievements and productivity to the success of the whole institution. At the Faculty of Mathematics, Institute of Scientific Computing, within the Dresden Center for Computational Materials Science (DCMS
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associated PhD topics is highly encouraged Your profile Completed university studies (Master/Diploma) in STEM fields with strong background on process modelling, applied mathematics, process engineering
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energy system model workflows Your Profile: Master’s degree in computer science, data science, natural sciences, economics, engineering, mathematics or a related field of study Huge interest in data
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) software for topology-informed biomedical image analysis and large foundation models. You will be responsible for Develop new machine learning algorithms for microscopy image analysis problems (2D/3D/4D/5D
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knowledge of energy system modelling or climate modelling Good knowledge of deep learning, PDEs or mathematical/numerical optimization methods Enthusiasm for challenging problems and interdisciplinary
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highly motivated doctoral student to join an ambitious project aimed at building machine and deep learning models to study the genetics of human disease. Funded as part of the Helmholtz AI program, the
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theories and numerical methods, carrying out and analysing field and remote sensing observations and conducting and analysing numerical model simulations. The PhD position is funded by the German Research