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Raman spectroscopy and mass spectrometry-based proteomics, along with cutting-edge machine learning approaches, to enable timely identification of high risk for the chronic wound formation Coordinate
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(proteomics, metabolomics, Raman) Combining Raman spectroscopy and mass spectrometry-based proteomics, along with cutting-edge machine learning approaches, to enable timely identification of high risk for the
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, programming and statistical evaluation Machine learning analyses: implementation of established and new workflows Coordination of activities with Consortium partners, including presentation of results
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pollution research Skills in a relevant programming language (e.g., R, Python) are essential, ideally including machine/deep learning and natural language processing Practical experience or good familiarity
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EDX). Additionally, a substantial part of this position involves the development and/or implementation machine-learning algorithms for data analysis. Your profile Master's degree in physics, materials
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and reactive transport modelling of these processes, including atomistic and pore scale simulations based on state of the art methods of quantum mechanics and machine learning. Your tasks This project
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Establishing reliable databases and a real-time streaming environment for processes to build a data-centric manufacturing ecosystem Empowering intelligent manufacturing by implementing machine learning-based
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translate your scientific ideas to tackle the challenges of fast and precise laser processing. Your scientific activities will be carried out in a close collaboration with the specialists in machine learning