79 machine-learning "https:" "https:" "https:" "https:" "The Open University" PhD positions at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 28.02.2029 Reference no.: 4985 Explore and teach at the University of Vienna, where more than 7,500 academics
-
or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell
-
of young scientists (Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https
-
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
-
: Application (cover letter) Vision for teaching and research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio
-
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
-
within the project AI4TECSWriting a doctoral dissertation in computer sciencePublishing research findings in leading international conferences and high‑impact journals in AI, machine learning, and
-
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
-
measure gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning
-
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