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certificate recommendation letters, if available (max 3) writing samples (max 3), e.g. PhD thesis and published papers Your Profile strong background in machine learning/artificial intelligence experience in
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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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/ ) at the Department of Statistics and Operations Research (https://isor.univie.ac.at/) , University of Vienna, is offering a PhD position in the area of Mathematical Statistics and Machine Learning, starting
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declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. About the team Join the Responsible Machine Learning (ML) Group at the Faculty
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Synergy Grant project SUSTECH – Accelerating Sustainable Technological Trajectories with Computational Chemistry and Machine Learning. Where you will be: The position is hosted by the research group
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, statistics, and microbial community analyses Experience with machine learning approaches, including the ability to apply relevant statistical models and predictive analytics to biological datasets, is highly
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Support in preparing research proposals and scientific publications, as well as in securing third party funding Assistance to principal investigators in supervising PhD candidates and diploma students
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Austrian Academy of Sciences, the Institute for the Cultural and Intellectual History of Asia (IKGA) | Austria | 4 days ago
anthropology Cultural studies » Asian studies Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 28 May 2026 - 23:55 (Europe/Brussels) Country Austria Type of Contract
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mathematics (diploma or master's degree) German language skills, if not first language: C1 Good computer skills Desired: PhD-theses in mathematics education Desired: Professional experience in teaching
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, DeepFields (using drones, airborne optical sectioning (AOS) -a unique synthetic aperture sensing technique developed by JKU-, and machine learning for harvest and damage estimation in agriculture), in