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, such as compilers, is an exceedingly complex task that often requires investments of hundreds of millions of dollars. This project aims to explore the extent to which LLMs can be harnessed to assist in
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) out to 2050. Tasks include compiling big-data inputs, developing CNOSSOS-EU compliant simulations, validating predictions with field/lab measurements and mapping future hot-spots. Ideal profile: honours
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2025-10-01, or according to agreement. Contact person: Anna Runemark anna.runemark@biol.lu.se Instructions on how to apply Applications shall be written in English and be compiled into a PDF-file
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soon as possible. Contact person: Sebastian Marquardt (andreas_sebastian.marquardt@biol.lu.se). Instructions on how to apply Applications shall be written in English and be compiled into a PDF-file
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at 23:59 hours local Danish time The position is funded by the German Research Foundation (DFG), project “Abstract Techniques for Programming Languages and Secure Compilation”. The project builds
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backend tool such as RC compiler, Design compiler, Cadence Genus, Innovus, Encounter Prior knowledge in at least one of the following areas as an advantage: deep learning hardware development memory
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-programme-in-electrical-engineering . Ready to apply? To apply for the position, please send the documents indicated below (in English, compiled in a single PDF) using our online recruitment system. To access
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Management and Analysis: Compile and analyze experimental data, comparing results with theoretical predictions to assess data quality. Technical Documentation: Prepare detailed reports and contribute
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. Laboratory and Field Testing: Evaluate sensor performance in laboratory settings and conduct field testing to validate sensor functionality. Data Management and Analysis: Compile and analyze experimental data
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for inspection of both micro and macro process accurately, that compiles faster than standard industry codes but delivers particle-in-cell accuracy has the potential to be a step-change technology