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Starting Date 1 May 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
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information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated solutions from care
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Mar 2026 - 22:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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, cyber-physical systems, real-time systems, autonomous systems, middleware, parallel computing, operating systems, Edge AI or AI in cyber-physical systems; Demonstrate strong analytical, programming, and
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Your tasks: Developing optimization algorithms for massively parallel hardware architectures such as AI
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through sensitivity, uncertainty, and scalability analyses. – Enhance the computational efficiency of large-scale optimization problems by exploring decomposition techniques, parallelization, and
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Declaration of interest regarding PhD project within the field of biomarker and therapeutic targe...
. The PhD student will work with patient cohort to perform biomarker analyses and statistical modeling of clinical outcomes. In parallel, the student will contribute to the development and validation of novel
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of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
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Framework Programme? Not funded by a EU programme Reference Number CNS2023-145425 Is the Job related to staff position within a Research Infrastructure? No Offer Description Descifrando la vulnerabilidad
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of statistical analysis is required. Strong programming skills in Python, R and Linux-based software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing