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documents uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Machine learning for 3D printed multifunctional metamaterials Description: The PhD topic focuses on the use
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Researcher (R1) Positions PhD Positions Application Deadline 8 May 2026 - 23:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework
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Computer science laboratory of Le Mans Université (LIUM) | Laval, Pays de la Loire | France | 5 days ago
Infrastructure? No Offer Description The Computer Science Laboratory of Le Mans University (LIUM) is offering a PhD position in Human Learning Environments (EIAH), starting in October 2026 (Laval site
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13 Apr 2026 Job Information Organisation/Company Inserm PACA Corsica Department iBV - U1091 Research Field Biological sciences Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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learning, probabilistic decision making) For your application, please submit: a brief motivation letter (max 1 page) your CV (including a list of publications, if available) transcript of records PhD
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Description Your Responsibilities We are looking for a highly motivated PhD student in the areas of Probabilistic Machine Learning and Neuro-Symbolic AI to contribute to the Cluster of Excellence “Bilateral AI
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Learning (e. g. combination of Language Models with Knowledge Graphs; development of truthful/citable Language Models) collaboration in national and international research projects, possibly with
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Director), head of the GIN's 'Brain, Behaviour and Neuromodulation' team. This multidisciplinary team brings together researchers, clinicians, engineers, PhD students and undergraduates around a common goal
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks? Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems