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will join an international and multidisciplinary team and will work in the project which aims to develop an innovative, modular, and partially bioresorbable wound filler for Negative Pressure Wound
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) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic hardware. Both projects
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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Vacancies PhD position on Stochastic geometric numerical methods Key takeaways Are you passionate about developing cutting-edge numerical algorithms at the intersection of geometry, stochastic
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will take advanced courses to build and deepen your skills, implement and evaluate algorithms, and develop your ability to write and present scientific work. We are a supportive team that will welcome
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 7 days ago
related to staff position within a Research Infrastructure? No Offer Description The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic
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the section that aims to develop the next generation of scientists trained in bioinformatics, AI/ML and data science who have a deep understanding of experimental biology as well. Responsibilities As a PhD
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currently work at the LCSB. We excel because we are truly interdisciplinary, and together we contribute to science and society. Your role Multi-omics data integration and workflow improvement Development and
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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Your Job: The accelerated development of advanced materials is essential for addressing major challenges in energy, mobility, and sustainability. Traditional trial-and-error methods in materials