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Field
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Experience in processing remote-sensing information and machine learning is an asset Capacity for interdisciplinary teamwork and excellent communication and presentation skills Ability to communicate in
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literature is approached from a wide variety of theoretical and methodological perspectives. The Class of Language Education investigates the processes and results of teaching and learning language, literature
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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, and therapy resistance mechanisms Ability to work independently and collaboratively within interdisciplinary teams Prior experience with network modeling or machine learning is a plus We offer
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learning, image analysis, and advanced computing to study relationships between structure and function. Keywords: Human Brain, 3D Atlas, Deep Learning, Temporal Lobe, Brain Function Entry Requirements
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, Machine Learning, Hyperspectral Cameras • Professional proficiency in written and spoken English Application process Send your application in English by email to amx@wzw.tum.de with the title “Research
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lines and cancer tissue samples. Selected candidates will be part of an international team of PhD students and postdocs that works at the forefront of biomedical research. The PhD student will be a member
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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation