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Field
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inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas publication and
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, nanofabrication, and computational electromagnetism. Strong coding (Python /MATLAB) and experimental aptitude is desirable.
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and
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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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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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assistantship. The ideal candidate will have a strong understanding of NLP, machine learning, and AI ethics, with proven skills in Python and frameworks like TensorFlow or PyTorch. Preference may be given
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literacy and basic programming skills (R, Python), or the willingness to develop these skills Analytical mindset, with a keen interest in advancing proteomic methodologies We offer Cutting-edge proteomic
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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to participate in international meetings Experience with conducting and documenting scientific experiments as well as processing and analyzing data (Python, Origin, Excel) Good communication in English, knowledge