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15 Apr 2026 Job Information Organisation/Company Graz University of Technology Department Institute of Machine Learning and Neural Computation Research Field Computer science Researcher Profile
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enterprises (SMEs). The postdoc will work at the intersection of cybersecurity, machine learning, and human centered system design, contributing to the research on privacy aware monitoring, attacker modelling
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
, leveraging on data analytics and machine learning to improve learning outcomes and engagement in the classroom, and Development of personal GPT-powered AI tutors that use the knowledge from (1) to provide
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. The postdoctoral researcher will conduct cutting-edge research in areas such as cyber-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position will involve
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science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 1 day ago
work algorithm design and their theoretical analysis numerical simulations writing scientific papers Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-09888 Requirements Skills
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, or similar, and a degree in Environmental Engineering, Environmental Science, or a related quantitative field. Position 2 will focus on large-scale data analytics and machine learning. Applicants should have
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision