55 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" PhD scholarships at Nature Careers
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Reference no.: 5202 Among the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around 7,500 academic staff members so far. They see
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of young scientists (Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https
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therapeutics. For more details see this review: https://doi.org/10.1016/j.trecan.2022.09.001 Please get in touch if you don’t have access to the review. The candidate will: Perform Oxford Nanopore sequencing
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such as sports programs, job ticket, company events, cafeteria, and emergency childcare For inquiries, please contact: Univ.-Prof. Dr. Markus Missler, T +49 251 83 50200, Markus.Missler@uni-muenster.de
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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of artificial intelligence, machine learning and/or deep learning experience in scientific publishing and presenting research results knowledge or experience in public health research Personal skills Independence
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The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we are seeking a highly talented and motivated PhD student within the field
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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mechanisms and kinetics to stabilize highly active but metastable surface motifs sustainable catalytic processes. Modeling Atomic Processes on Nanoparticles Develop atomistic models and machine-learning
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sciences or similar Strong interest in single-cell nucleus RNA-seq techniques / Perturb-Seq / Crop-Seq Willingness to learn computational biology/bioinformatics High motivation for scientific work and