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10th October 2025 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Machine Learning & Signal Processing for Industrial Applications Apply
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distribution modelling Experience with spatial analysis and mapping tools (e.g., QGIS, ArcGIS, or spatial packages in R/Python) Interest or experience in applying AI or machine learning methods to ecological
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for the position. Preferred selection criteria Scientific publications are an advantage Experience in research project works Good knowledge and experience in the use and development of machine learning algorithms
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spatial analysis and mapping tools (e.g., QGIS, ArcGIS, or spatial packages in R/Python) Interest or experience in applying AI or machine learning methods to ecological questions Personal attributes: Strong
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of the position: Follow the faculty’s PhD program Plan design and carry out experiments and their analysis Prepare research articles for peer-reviewed journals (min. 3) Be willing to present results at conferences
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open up exciting career opportunities? Are you interested in cable technology and condition monitoring and do you have a strong competence in signal processing and machine learning? As a PhD candidate
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foundation for theory-guided catalyst design e. g. by machine learning approaches. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within
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the Head of the Computing Group. Duties of the position Acquire and maintain cutting-edge knowledge of the field Coordinate with the supervision team to agree on research directions Actively participate in a
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, Amsterdam and Freiburg, will analyse the impact of blockades on households, states, corporations and the international order; on the development of political and military strategy; on how the wars were
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interdisciplinary center with joint efforts in theory, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by