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applications for a PhD Student or Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and
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HBIGS Heidelberg Biosciences International Graduate School | Mannheim, Baden W rttemberg | Germany | 18 days ago
as innovative cellular therapies due to their high natural cytotoxicity and their potential to be used as off the shelf products. Chimeric Antigen Receptor (CAR) bearing NK cell-based therapies have
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, physics, or similar, with a strong Machine Learning or simulation background In depth practical experience in at least one programming language (preferably Python) Ideally, some practical experience in
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control software and machine learning expert. How you will support us: ▪ You will take on responsibilities in the field of control and operation of high-coherence superconducting quantum circuits, with a
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, 27 employees and students are working together with renowned industry partners on the further development of LPBF for a wide range of industrial applications – from new machine concepts and innovative
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. He/she/they will learn and apply state-of-the-art molecular and cell biology technologies established in our team, ranging from in vivo disease models to multi-omics and single cell analysis
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do The complete design and implementation of analog circuits including
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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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