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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
collaboration and cooperation - intra-institute, national and international, interdisciplinary opportunities to develop – on the job and towards other positions an international environment – everyday contact
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The culturally diverse Research Group “Plant Architecture” is primarily interested in basic research questions concerning how cereal inflorescences, called spikes, develop and how this contributes
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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Institute (https://www.mdsi.tum.de/). The Position Plan, develop and test novel computational models for the analysis of digital pathology image data. Collaborate with pathologists and other domain experts
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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You
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MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms
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opportunity to be an active part in the establishing of our department Comprehensive training programs and individual opportunities for personal and professional development Access to a strong research network
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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on quantum dot qubits Good software development skills Experience with sub-Kelvin cryogenic techniques Strong aptitude for mentoring students Good communication skills A strong drive to conduct ambitious
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at local, regional, national and European scales across the terrestrial, freshwater and marine realms. For this, a suite of thematic VREs will be developed that allow an easy-to-use application of models