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posts Starting date: 14.08.2026 Job description: Postdoctoral Researcher - Probabilistic Deep Learning for Urban Air Quality (AEON-UP) Referenzcode: 1056 Arbeitsort: Geesthacht Bewerbungsfrist: 03.09.2026
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«Detaljer» for å lese mer om dette og tilpasse dine valg. Om cookies Bare nødvendige cookies UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine
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position in probabilistic methods for foundation and world models Uppsala university Share: Facebook Twitter LinkedIn Email Application deadline October 15, 2026 Read more and apply SciLifeLab / Careers
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Max Planck ETH Center for Learning Systems | New Germany, KwaZulu-Natal | South Africa | 25 days ago
, Natural Language Processing, Neuroinformatics, Optimization, Physical AI, Probabilistic Models, Reinforcement Learning, Robotics, Security and Privacy, Smart Materials, Social Questions, Soft Robotics
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learn and grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. Dr Yingzhen Li ( https://yingzhenli.net ) and her
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Position Summary We seek outstanding candidates from all areas of statistics and applied probability. Candidates who develop innovative statistical or probabilistic theory and methodology, as well as
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on the design and development of mathematical, probabilistic, and statistical frameworks for drawing inferences from complex biological data in collaboration with scientists at the Snow Centre for
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Helmholtz Association of German Research Centres | Geesthacht, Schleswig-Holstein | Germany | 2 months ago
data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Postdoctoral Researcher - Probabilistic Deep Learning for
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monitoring, earthquake physics, ground-motion modelling, site response, exposure and vulnerability, and probabilistic seismic hazard and risk assessment, working to strengthen Singapore’s national capabilities
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institutions. Predictive models with quantified uncertainty will be developed and validated, together with probabilistic consequence models to support risk-based decision-making. A structural optimisation