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of specific 2D functionalization strategies, we have a broad and flexible focus when it comes to the employed methodologies (empirical models, DFT, many-body perturbation theory, machine-learning) the target
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with researchers from machine learning and fire safety and material science in a truly interdisciplinary environment. Co-author scientific papers aimed at high-impact journals. Participate in
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. Additional knowledge and experiences within the following areas are highly appreciated: data analytics, machine learning, federated learning, and data privacy. Outstanding spoken and written communication
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nature. The groups of the Section cover a wide variety of subjects ranging from personalized medicine where we, amongst others, predict the optimal treatment based on an individual’s genome, to machine
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microscopy and automated data analysis via machine learning we aim at creating structure-functionality correlations for tailored materials. In collaboration with theoreticians, we aim at extracting data from
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available from 1 October 2024 or later. You can submit your application via the link under 'how to apply'. Title PhD position in machine learning to predict nitrogen leaching at field level Research area and
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multiscale modelling techniques, machine learning methods, and self-driving laboratories to accelerate the discovery of novel materials for energy applications. The candidate will work in a highly
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central goal of the project is to develop explainable machine learning models that allow insight into the interaction between genetics and other types of data that can be used for developing tailored
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event-based simulation and a variety of machine learning techniques. A strong curiosity and interest in current and future mobility challenges, especially those related to logistics. Excellent written and
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fascinated by the possibilities of employing neural networks in computer vision? Would you like to develop methods with the potential to address critical societal challenges? Would you thrive in an innovation