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Degree PhD Course location Potsdam In cooperation with The innovative PhD and postdoctoral programme is offered in conjunction with the Faculty of Digital Engineering, a faculty jointly founded by
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
of interest, skills and previous experience, motivation for Doctoral Research, future goals, and why you are applying to this project. On the application system select Programme of Study – PhD
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
leading universities, research institutes, and industrial partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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architectures, or diffusion models. In close collaboration with clinical partners, you will translate your research into practical, translationally relevant applications, contributing to advances in personalized
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environment at the University of Kassel. Its offers are open to all academics and artists in the qualification phase at the University of Kassel. Further training / transferable skills Kassel Graduate Programme
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., Architectural, Electrical, Mechanical or Industrial Engineering, Autonomous Systems, Computer Science, Medical Engineering, and Human Psychology) More detailed information about the Rules related to DTU’s PhD
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architectures [5] and Neural ODEs [2], creating a cohesive end-to-end hydrological model. Transformers will be utilized to pre-process and analyse the raw input data, capturing essential spatial and temporal