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Inverse Design of Perovskite-Based Materials for Photovoltaic Devices Your Job: As a PhD candidate, you will develop and deploy an artificial intelligence (AI)-driven approach to streamline high-throughput
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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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-Curie Doctoral network METAMIC 3 - Metaproteome-based leveraged microbiome management in the context of One Health. METAMIC 3 is funded by the European Commission under the Horizon Europe framework
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is beneficial We encourage you to apply even if you do not meet every requirement. We value diverse perspectives and are committed to finding the best candidate, recognizing that growth and learning
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include: Implementing the classical components of the Sample-Based Quantum Diagonalization [1] method within the MiMiC multiscale simulation framework Developing and integrating a communication interface
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The position is to be filled within the Marie Skłodowska-Curie Doctoral network METAMIC 3 - Metaproteome-based leveraged microbiome management in the context of One Health. METAMIC 3 is funded by the European
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The Network Analysis and Modelling uses machine learning to investigate how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. We are seeking a
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Max Planck Institute for Biology of Ageing, Köln | Koln, Nordrhein Westfalen | Germany | 31 minutes ago
international research environment at one of Europe's most prestigious ageing research clusters, comprising 49 interdisciplinary research groups Initial work contracts of at least three years with payment based
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computing and application of AI-based methods Basic knowledge in photovoltaics and solid-state materials for energy application Ability to work individually and in the team, to effectively collaborate and to
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
-based digital twin, which can predict the assortment and sequence of two-dimensional all-DNA motifs required for the engineering desired tessellation patterns at the nanometer scale. We will use available