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) für Lehre, Studierendenbetreuung und Publikationstätigkeit. We Offer Interesting area of responsibility Collegial and friendly working atmosphere Flexible working schedule (e.g. possibility for home
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,• orchestration and usage of services,• data streams, data management and data pipelines• scalable runtime systems• scheduling and optimization for runtime, latency and throughputfor Cloud and Edge
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of services,• data streams, data management and data pipelines• scalable runtime systems• resource management,• scheduling and optimizationfor Cloud and Edge infrastructures.The research of these PhD positions
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atmosphere Flexible working schedule (e.g. possibility for home office, paid lunch break - depending on working hours) International training and teaching opportunities Seal of quality for in-house advancement
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Deadline The deadline for applications is 5pm on the 19th November with interviews scheduled to take place on 25th November.
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During the publication period, applications will be handled continuously. If the vacancy is filled, it will be closed prematurely. The first round of interviews is scheduled to take place in
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until 27 October 2025. The first interviews are scheduled for 10 and 11th of November 2025. Additional information form For this position, you must fill in a mandatory form which will allow us to have a
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of family and career Consideration of personal wishes regarding the weekly working schedule Information regarding the position Paygrade E13 according to TVöD VKA-K. Your job grade depends on your job
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | about 10 hours ago
recruitment board reviews all submitted applications. Candidates are shortlisted for the next stage based on eligibility and qualifications. Online Interviews: Scheduled for mid December 2025 Shortlisted
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). The Computer Science group is looking for students to work on one of the following projects Distributed Intelligence for Self-Organising Cloud–Edge Infrastructures Carbon-Conscious Resource Scheduling for AI Workloads