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experience in machine learning and parallel computing Good organisational skills and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow
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forming a moist microbial rhizosphere habitat. Your work will build on the process-based soil-rhizosphere-plant model, developed at the Agrosphere Institute, which integrates a 3D functional-structural
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to make them usable for transparent energy system analyses. You will process and semantically enrich the collected data before it is integrated into a knowledge-graph-based metadata platform. In
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sorption and co-precipitation processes, as well as the reversibility of these processes. Furthermore, the effects of heterogeneities in the mineral composition and distribution on radionuclide migration in
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. Your main responsibilities will include: Fly drones and process data of multispectral, thermal and LiDAR sensors Implement approaches to integrate drone data into an existing agroecosystem data
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Your Job: At the Institute for a Sustainable Hydrogen Economy - Catalytic Interfaces for Chemical Hydrogen Storage (IHE-1), we investigate fundamental mechanisms and processes of chemical hydrogen
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neurological diseases. Before PET imaging, a radiotracer is injected into the patient to display metabolic processes in the body. At the Institute for Neuroscience and Medicine (INM-4), we are developing
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the initial stage, you will synthesise ring polymers using anionic polymerisation techniques. You will also develop new linking chemistries and purification processes. The physics part of the project will focus
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in field conditions. The samples can now be processed and analysed. We are looking for a motivated intern to work on the processing of root samples. The intern will learn and use root analysis methods
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Your Job: Literature research and process understanding of higher alcohol synthesis Catalyst preparation, characterization, and performance testing Reaction parameter optimization (temperature