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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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. 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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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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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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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