149 phd-in-architecture-interior-design-built-environment PhD positions at DAAD in Germany
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well as for humans (Wei et al., 2024; Zhan et al., 2018). Improper application of glyphosate causes its accumulation in terrestrial and aquatic environments as glyphosate can bind to soil particles leading it to be
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Description In the Leibniz Institute of Plant Biochemistry, the research group Symbiosis Signalling invites applications for a PhD position in biology (m/f/d) (Salary group E13 TV-L, part-time 65
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) • The successful candidate will have the opportunity to work towards a PhD Required qualifications: • Completed university degree (M.Sc. or comparable) in biology or a related field • Solid knowledge of molecular
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Description Microplastics are ubiquitous in the environment due to their versatility and the large quantities in which they are produced and used. At the same time, global consumption and industrial
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research environment. Access to state-of-the-art equipment and facilities. Member of the integrated Research Training Group for dual mentoring, comprehensive doctoral training program, weekly seminars
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) • The successful candidate will have the opportunity to work towards a PhD Required qualifications: • Completed university degree (M.Sc. or comparable) in biology or a related field • Solid knowledge of molecular
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offering PhD positions for students with a background in data science, computer science, computational science, or a domain science with a strong focus on computational science and an interest in training
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in near real-time. This includes exploring and advancing electrochemical sensor technologies. The PhD project will lay the foundation for the digital transformation of water infrastructure, enhancing
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Description Doctoral Research Position (m/f/x) Reference number W2708, salary in accordance with the German TV-L salary scale1, pay grade: E13 TV- L, duration of employment: 3 years, volume of employment: 50 % of standard working time. Workplace/Department: Institute of Virology, Saarland...
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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