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data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional
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machine learning, computer vision, and materials science. The focus of this position is on development of neuro-symbolic models for the effective behaviour of the complex microstructure of recycled
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performance. However, calculating defect formation energies and migration barriers using first-principles methods remains a major bottleneck in the materials discovery process. To address this, we will develop
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Biosensors carries out research on the integration of nanotechnology methods, tools and materials into low cost, user friendly and efficient sensors and biosensors. The main objective of Merkoçi group is to
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for estimating sustainable city size by identifying key determinants (e.g., accessibility, agglomeration, emissions, equity), testing spatial boundary sensitivity, and broadening sustainability
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This PhD project aims to advance Safe and Sustainable by Design (SSbD) pharmaceutical manufacturing by integrating cutting-edge methodologies, including computer-assisted retrosynthesis, end-to-end
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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collaboratively on a multidisciplinary team. • Synthesize and manufacture unique design architectures comprised of coatings and bulk materials using a variety of manufacturing methods. Materials synthesis may
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external experimentalist. You should have expertise in at least one of the following areas (i) Non-adiabatic chemical dynamics, (ii) Physics of charge transport in the solid state, (iii) QM/MM methods, (iv
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early