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pathways and degradation mechanisms at inorganic–organic interfaces. The position is hosted at the Fritz-Haber-Institut (Berlin) in a close partnership with the MPI Magdeburg, contributing to algorithm
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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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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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research school on secure distributed computing (SeDiC) is proposed. SeDiC aims to tackle the challenges of exchanging and computing data across a network of interconnected systems. It addresses scalability
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these micropollutants, per- and polyfluoroalkyl substances (PFAS) are of particular concern. Like microplastics, PFAS are highly persistent, mobile, and widely distributed, even in remote areas. Both substance groups
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable