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Field
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to consider the Ericksen—Leslie model with stochastic fluctuations and investigate it in terms of existence of solutions and their approximation, the associated large deviation principle and the simulation
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an interdisciplinary research environment aimed at achieving a comprehensive understanding of geomagnetic field evolution across different timescales, including both stable and extreme periods. This will involve working
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, collaborating with several research groups working in related fields, particularly in algebraic geometry and algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our
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relationships should then be extrapolated to different ecosystems in European seas, also in support of management efforts. Our team is internationally renowned in the field of trait-based marine modeling. Within
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positions and general information, please email andra.milcu(at)uni-kassel.de In the event of any differences between the German and English versions of this call for application, the German wording is legally
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differences between the German and English versions of this call for application, the German wording is legally binding. Our Offer: As an employee of the University of Kassel you will be offered an interesting
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(centiles) and individual deviation scores. The work spans multi-site morphometry, scanner harmonisation, model comparison, and external validation. You will work closely with the project lead at the
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. We benefit from the different expertise, perspectives and personalities of our staff and welcome every application from qualified candidates, irrespective of age, gender, ethnic or cultural origin
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. The project’s overarching goal is the development of digital quantum algorithms for the simulation of non-abelian lattice gauge theories. We are looking for highly motivated individuals, with the desire
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based