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real-world problems. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading
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<span lang="en">Leipzig University</span> | Leipzig, Sachsen | Germany | about 23 hours ago
energy that drives the particles
- Track hundreds of interacting particles with machine-learning methods and test fundamental thermodynamic bounds on control
- Work closely with the Tel
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Doctoral Researcher in Trustworthy Machine Learning Employer AALTO UNIVERSITY Location Finland (FI) Salary 3168 EUR per month Closing date 31 Oct 2026 View more categories View less categories Job
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PhD student focusing on Machine Learning of Geometric Representations at ISY Linkoping Reference number LiU-2026-03853 We have the power of over 50,000 students and co-workers. Students who provide
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-supervised machine learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to train models on synthetic data generated by biophysical
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geometry, number theory, data. * Qin Li: Scientific computing, machine learning, inverse problems. * Hanbaek Lyu: Combinatorics, probability, optimization, and machine learning. * Laurentiu Maxim
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 10 hours ago
monthly salary of EUR 2.832,10 paid 14 times a year Closing date 1 Nov 2026 Website https://aithyra.at/ View more categories View less categories Discipline Biomedicine , Chemistry , Computing
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Apply online now : https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=29009 Data Scientist/Computational Scientist – Multimodal and Multi-Agent AI for Precision Oncology (m
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and data will be lost. Posting Summary Logo Posting Number RTF00167PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title
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design of hybrid electrical–optical computing architectures and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among