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-based metadata platform that you will help develop. In collaboration with stakeholders from energy research, you will develop methods to increase data and software interoperability, enabling the automated
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, optimize the control algorithms and compare the results. The candidate should be able to develop software programs (patient simulator and graphical user interface using Matlab), to test and validate
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ECUs are reshaping how connected and autonomous vehicles are designed and validated. The focus is shifting toward creating high-fidelity virtual ECUs that support software-defined vehicle architectures
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at: Department of Computing Science Project description Our societies rely on computer systems and on software stacks. Unfortunately, software systems contain bugs and vulnerabilities which can be exploited by
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topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured data PhD Student Role: Under the direction of their supervisor, the candidate will
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scientific and research tasks in the field of social sciences and actively publish in internationally recognized journals on WoS Experience with tools and software for qualitative data analysis is an advantage
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your software engineering skills to redefine manufacturing? This exciting PhD project is for you if you want to develop innovative techniques to ensure industrial robots are reliable, safe, and adaptable
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bioinformatics pipelines for the metabolomics data analysis and visualization of metabolomics data, support the integration of software tools for data (pre-)processing, biomarker discovery, and predictive
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 month ago
to research in computer science and mathematics. 400 R&D engineers support research scientists by developing software tools and platforms that help the research process and the experimentation. Spirals
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with e-CALLISTO instruments or Software-Defined Radios (SDRs). · Familiarity with machine learning for astrophysical data analysis. · Knowledge of solar radio data pipelines and event classification