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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | about 1 month 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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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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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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technology. ▪ Close connection to the activities of the Munich Quantum Valley with its main goal to build a quantum computer based on different platforms, to develop suitable algorithms and applications, and
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Aerospace Centre (DLR), will conduct research on 20 research topics with 25 PhD candidates within the next years. The following main research goals are pursued by this Center: (1) develop a new set of
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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of infection, immunology, and adaptation of immunity across life. We aim to recruit a bioinformatician to complement our expertise and to develop an individual research focus within our research team. We offer
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Chair of Biological Imaging 11.07.2023, Wissenschaftliches Personal We now seek a highly qualified and motivated PhD student (f/m/d) to design, develop, and test novel optoacoustic sensing platforms
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Your Job: Random unitaries are a ubiquitous tool in quantum information and quantum computing, with applications in the characterization of quantum hardware, quantum algorithms, quantum cryptography
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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