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Field
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the training of state-space models Collaborate closely with our internal partners at PGI-14 (Neuromorphic Hardware Nodes) and international academic and industry partners Publish research articles and regular
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developing and using dedicated tools and processors Contribute to our sparse auto-differentiation libraries to accelerate the training of state-space models Collaborate closely with our internal partners
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 2 months ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using
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-Checking, Argument Mining, Automated Planning, and Decision-Making. Training, domain adaptation, and evaluation of cutting-edge LLMs and Multi-Modal models in the cloud and on premise. Software Engineering
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data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using programming languages like R, and/or Python. You have excellent
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Neuroscience uses theoretical approaches from this broad range of disciplines to integrate experiment, data analysis and modelling in order to understand the brain. Furthermore, it makes a scientific language
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of FPGA designs, including timing analysis, code coverage and coding rule checks Support FPGA integration on target hardware Create design documentation in compliance with internal and external normative
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microscopy. Root architectural phenotyping. Plant and root physiological measurements to determine the function of root phenotypes. Functional-structural plant modeling to determine the benefit of root
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January 2026 (6pm CET). The deadline for referees to submit reference letters is 14 January 2026 (6pm CET). Please check our website https://www.molgen.mpg.de/IMPRS/application for more details. Tuition
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the University of Stuttgart, and with the right to award doctorates, it represents an innovative and unique organisational and structural model for the doctoral phase by connecting research and qualification as