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Degree Doctoral degree (PhD) Course location Nürnberg Teaching language German English Languages The course languages are English and/or German. Programme duration 6 semesters Beginning Only
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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research
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-based products, is among the areas that more quickly is adopting AI. Machine learning (ML) algorithms are being developed and integrated in microscopes for its autonomous operation and in software
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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position (26 hours per week) starting from 01.06.2025 limited to 48 months at our location in Müncheberg as PhD Position in remote sensing for Soil organic carbon and tillage assessment (f/m/d). Your tasks
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Max Planck Institute for Human Cognitive and Brain Sciences • | Leipzig, Sachsen | Germany | about 1 hour ago
the IMPRS CoNI is English, German language skills are not mandatory. For PhD applicants, a minimum score required for a TOEFL paper-based test is 550. The minimum score for the TOEFL computer-based test is
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CMFI Cluster of Excellence iFIT Cluster of Excellence Machine Learning CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC-TRRs
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possible to train a machine learning model to identify tumor-reactive T cells infiltrating any tumor type, cutting months off the time it takes to develop personalized cell therapies for patients. We
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methods, including coarse-grained and atomistic molecular dynamics, systematic coarse-graining, machine learning, and continuum solvers for hydrodynamics, such as the lattice-Boltzmann method. Among other
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation