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the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by the German Research Foundation (DFG), at the University
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for climate-positive and CO2-negative processes. What you will do Optimization of the pilot plant for the combustion of solid fuels (biomass, waste) with oxygen and subsequent CO2 capture Development of models
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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“Multi-modal proteomics to understand and overcome niche-related adaptive drug-resistance mechanisms in FLT3-mutated AML“ we are looking for a PhD student. The project is is part of the SFB/CRC 1709
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% of full time E13. The position is associated with the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by
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research. For the working group “Past and Future Earth” (PATH) within the Research Department “Earth System Analysis”, PIK is offering a PhD position (m/f/d) (Position number: 21-2025 PhD PATH) in the field
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Kohlenforschung, Mülheim/Ruhr Ruhr-Universität Bochum Universität Duisburg-Essen Teaching language English Languages The doctoral programme is conducted entirely in English. Full-time / part-time full-time
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Degree Dr rer nat (Doctor of natural sciences) Course location Münster Teaching language English Languages Courses are held in English. Mode of study Less than 50% online Programme duration 6
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, computational model development, data processing, and code implementation in close cooperation with scientists. The position is limited to 3 years. Equal opportunity is an important part of our personnel policy