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operation · Application of artificial intelligence or machine learning in energy or engineering systems 5. Strong programming and modelling skills using relevant tools such as Python, MATLAB
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brainstorming, followed by implementing a prototype solution in Python (and several iterations of going back to the blackboard to fix various issues). A working prototype solution is eventually implemented in
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field. - Strong background in AI, with expertise in model distillation and optimization techniques - Proficiency in Python and related AI tools and frameworks. - Hands-on experience with MLOps practices
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and commercial tools (e.g. XCMS, MZmine, Compound Discoverer, GNPS, SIRIUS, etc). Proficiency in one or more programming languages (e.g. R, Python). Experience with continuous integration and best
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-intensive field Proficiency in Python (or R), version control, and clean code practices Experience with omics data analysis and integration Hands-on expertise in developing and fitting executable models
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mathematics e.g. calculus and probability Ideally experience with command line and sequence analysis Good programming skills (e.g. R, Python, C/C++) Knowledge of basic statistics and application in R or similar
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, digital twins, or related areas Excellent publication record in high-quality journals and/or conference proceedings Excellent programming skills, particularly in Python and/or C/C++; hands-on experience
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-Guided Machine Learning. Essential Interview / Application / Test Strong software development skills (for example, in Python, MATLAB, C++, usage of HPC facilities) Essential Interview / Application / Test
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-centred AI, digital twins, or related areas 3. Excellent publication record in high-quality journals and/or conference proceedings 4. Excellent programming skills, particularly in Python and/or C
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conferences. It is essential that you hold a PhD/DPhil in computational biology, genomics, bioinformatics, computer science, statistics, or a related field together with strong programming skills in Python, R