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- Technical University of Munich
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Field
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for autonomous optimization and design of materials, structures and processes Multimodal scientific AI – integrating language, images, sensor data and simulations Alignment for scientific AI – scalable oversight
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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CFD simulations, led to a shift from performance towards efficiency, defined more precisely as minimisation of computer resources required for a given simulation. Concomitantly, the objective
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projects in at least one of the following areas: machine learning, NLP/LLM, data analysis, software development, or medical data processing Willingness to familiarize yourself with medical standards, data
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imaging. Your Profile: The successful applicant must have the following: • Master’s degree in physics, biophysics, biomedical engineering, computer engineering or electrical engineering. • Excellent track
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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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important roles: data management and engineering, machine learning and data analytics, signal and image processing, algorithm design, optimisation and simulation, software engineering and automation and
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are interested in combining disciplinary knowledge with the skills of a data scientist and working at the interface of bioinformatics, medical informatics, databases, data mining, machine learning, applied
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research excellence and the ability to pursue a doctoral degree at TUM Documented experience in machine learning and/or natural language processing with deep knowledge of LLMs, RAG and agent-based AI systems