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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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://martilab.com/ . Trains under the supervision of Prof. Edward Marti including (but not limited to) the following activities: Build and optimize ultrasound rigs and imaging of neonatal and adult rodents (mice and
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Job Description: Prof. Karmel S. Shehadeh, Gabilan Assistant Professor of Industrial & Systems Engineering is seeing to hire a Postdoctoral Scholar to work on fundamental or applied research in Prof
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motivated Post-Doctoral Associate to join our team with a strong background in robot control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance
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under the supervision of Prof. Edward Marti including (but not limited to) the following activities: Build and optimize ultrasound rigs and imaging of neonatal and adult rodents (mice and rats). Develop
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this position will be to provide in vitro biochemical, biophysical and structural data from novel mutants of the EGFR kinase domain to drive and validate algorithmic development. Organisation The vacancy is
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
Job description We are seeking two highly motivated postdoctoral researchers to join our research team based in the Van de Peer lab, under the supervision of Prof. Dr. Zhen Li, Assistant Professor
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(IOPP) at Central China Normal University (CCNU) invites applications for a postdoctoral research position in lattice gauge theory, with an emphasis on computational and algorithmic advancements
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live in. Your role This is a fully funded position for 12 months in the HEXAPIC project, which is conducted in collaboration with Prof. Leon Kos's team at the University of Ljubljana. The HEXAPIC project
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past