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, the details of the process are not yet fully understood. Mechanistic learning, the combination of mathematical mechanistic modelling and machine learning, enables a data-driven investigation of the processes
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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Description The duration of the position is limited to three years. Your duties: • Participate in the DFG-funded research project: “Emergence and self-organisation of bacterial metabolism in consortia of cross-feeding bacteria ” • Generate and characterize bacterial mutants • Perform coculture...
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Description The duration of the position is limited to three years. Your duties: • Participate in the DFG-funded research project: “Emergence and self-organisation of bacterial metabolism in consortia of cross-feeding bacteria ” • Generate and characterize bacterial mutants • Perform coculture...
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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, and e-mail addresses of two references) by November 13, 2025 (stamped arrival date of the university central mail service or the time stamp on the email server of TUD applies), preferably via the TUD
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transcripts, at least one recommendation letter and contact information of at least two references. To apply, please send all the required documents in a single .pdf document by e-mail (Subject: PhD application
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for Infection Research GmbH, Human Resources Department, Inhoffenstr. 7, 38124 Braunschweig, or by e-mail . If you send your application in electronic form, please provide a summary in one single (1) pdf
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17Zipcode37077CityGöttingen Contact details Tel:+49 551 5176-100 E-Mail: golestanian-office at ds.mpg.de Web: https://www.ds.mpg.de/lmp Legal notice: The information on this website is provided to the DAAD by third parties
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Golestanian Address StreetAm Faßberg 17Zipcode37077CityGöttingen Contact details Tel:+49 551 5176-100 E-Mail: golestanian-office at ds.mpg.de Web: https://www.ds.mpg.de/lmp Legal notice: The information