13 algorithm-"Prof"-"NTNU---Norwegian-University-of-Science-and-Technology" positions at Nature Careers
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. Applicants should have strong expertise in computational analysis of electrophysiological data as well as proficiency in large language models and machine learning algorithms. First-hand experience in
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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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Networks, and ICT Services & Applications. Your role We offer a dynamic postdoctoral research position who will join the TruX Research Group headed by Prof. Dr. Tegawendé F. Bissyandé. The successful
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Networks, and ICT Services & Applications. Your role The APSIA Group (Applied Security and Information Assurance), led by Prof. Peter Y A Ryan, has recently been granted the project "Quantum LDPC codes with
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supervised by Prof. Dr. Jun Pang. For further information or inquiries, please contact Prof. Dr. Jun Pang directly. Your profile PhD in Computer Science or a related discipline, coupled with outstanding
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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The Institute for Infection Prevention and Control (IPC, Head Prof. Dr. Philipp Henneke) is looking as soon as possible for a Bioinformatician (m/f/d, PhD) with focus on the analysis of large
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live in. Your role This is a fully funded position for 12 months in the HEXAPIC project, which is conducted in collaboration with Prof. Leon Kos's team at the University of Ljubljana. The HEXAPIC project
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of multi-omics data sets generated with innovative high-throughput technologies used in Research Sections I and II (e.g. sensory, metabolome, proteome, and transcriptome data) by using efficient algorithms