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Field
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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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. 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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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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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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Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C++); Proven interest in
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. You have strong programming skills in C/C++, Python, and/or Matlab. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP, ADALM-Pluto) and software (e.g
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/or interest in programming languages (e.g. MATLAB, Python, R) and software platforms such as Autodesk, ALLPLAN, ANSYS, REVIT, Tekla, Rhino, Grasshopper, etc. Basic knowledge in the areas of signal
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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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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