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Field
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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- reference number MB-2025 At Helmut Schmidt University / University of the Federal Armed Forces Hamburg (HSU/UniBw H), Faculty of Mechanical and Civil Engineering, Professorship for Materials Engineering (Prof
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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(link ). Our expertise comprises various aspects of quantum computing, from quantum chemistry and optimization to machine learning (ML). With our activities in quantum-assisted optimization, we
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of study. You have knowledge of artificial intelligence and its application in the analysis of company data. You have experience with Generative AI technologies (e. g. GPT models, machine learning). You are
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advanced statistical/chemometrics and machine learning tools, iv) to couple metabolome data with other omics datasets (e.g., genomics, lipidomics, metallomics, and others). Main target areas are drug
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such as ecology, economy and social sciences. ZMT aims to use data science tools, including computer vision and deep learning, for the study of rapid changes in tropical coastal socioecological systems
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13.02.2025, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Computer Engineering at TUM has open positions for a doctoral researcher (TV-L E13 100%, 3-4 years
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students who would like to write their final thesis in the field of machine learning / computer vision. The primary goal of this master’s thesis is to develop an algorithm that can accurately and efficiently
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maintain pipelines for the analysis of high-throughput sequencing data, including RNA-seq, ChIP-seq, ATAC-seq, and single-cell and spatial omics. Integrate machine learning and large language models (LLMs