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effectively within interdisciplinary and multicultural research teams. We strongly encourage applications from computer scientists with substantial experience in deep learning and related methods, even if
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, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences (AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians
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and health. The lab conducts method-driven research in natural language processing (NLP) and artificial intelligence (AI), with an emphasis on LLM reasoning, uncertainty quantification, interpretability
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., human, organizational, infrastructure) to conduct research and support workforce development efforts on their campuses. This collaborative research project will use mixed methods to investigate
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quite new within the field of computer vision. The neuromorphic design allows for a much higher acquisition frequency but most and foremost much longer acquisition time spans. This makes it ideal
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statistical and computational methods motivated by potential applications to observational studies. The work will involve both methodological research with biostatistics faculty and collaboration with clinical
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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of computer-aided tools for chemical and biochemical product and process modeling, process synthesis, design, analysis and operation. The tools are applied in the chemical, petrochemical, pharmaceutical
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enables the application of phase contrast methods with a spatial resolution below 50 nm. Join the Cluster of Excellence "BlueMat: Water-Driven Materials" (www.tuhh.de/bluemat ) and contribute to one
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wildfire simulations to design and assess scenarios for risk mitigation. For Active-Frie problem domain, we seek candidates with strong background in analytical and computational methods related to physics