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motivation for scientific research, curiosity, and a proactive attitude towards learning new methods Excellent presentation and communication skills Good time and task management Independent work capability
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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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flat interconnect AI compute clusters. Machine learning clusters and artificial intelligence (AI) training have become increasingly popular in recent years. The recent introduction of OpenAI’s ChatGPT
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
., Revenko, A., Teije, A. T., & Harmelen, F. V. (2023). Combining Machine Learning and Semantic Web: A Systematic Mapping Study. https://doi.org/10.1145/3586163 [2] Benoît Combemale, Pascale Vicat-Blanc
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assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and quantitative security assessment
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researchers from diverse backgrounds. Good communication skills and a willingness to learn are important for working effectively within and beyond the consortium. Candidates should hold a Master’s degree in
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| Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.09.2029 Reference no.: 6194 Explore and teach at the University of Vienna, where more than 7,500 academics thrive
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal