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Field
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tools (e.g., Python programming) is an advantage. You enjoy working in an international team and have good communication skills. Proficiency in spoken and written English is required. For more information
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. Experience with high-throughput data analysis, including single-cell RNA sequencing, spatial transcriptomics, or computational immunology using R or Python, is a plus but not required. Excellent organizational
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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programming (MATLAB/Python) and/or structural analysis software (e.g., OpenSees, ABAQUS) is highly desirable Willingness to participate in experimental testing campaigns and multi-national secondments
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area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise your own workload
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gas chromatography. It is also probable that you will use CAD (e.g. for 3D printing), programming (e.g. Python) and MEMS chip design and fabrication. You should therefore have a desire to learn new
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, mathematics, computer science or similar; Solid mathematical and analytical skills, including signal processing, optimization, or information theory; Experience in programming, e.g., in C++, Python or Matlab
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area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise own workload and
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, particularly GBS, continuous-variable QC Experience with numerical simulation, statistical estimation, or probabilistic modeling. Programming proficiency (Python, Matlab or C++), especially for numerical
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or machine learning. Excellent programming skills (e.g., MATLAB, Python, or ROS), a strong publication record, and an ability to work collaboratively in multidisciplinary environments are essential. Prior