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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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, economics, business informatics, data science, or a related field, • with a strong passion for entrepreneurship and/or family enterprise research, • with a solid knowledge of empirical research methods
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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of buildings and built infrastructure, is thus the focus of the Institute. The Nemetschek Innovation Foundation supports the Institute, and TUM embeds it in its large network of data-centered sciences
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(e.g. Python, R, …). Familiarity to work on a Linux computing cluster (HPC). Preferably experience in working with large medical image data. Vivid interest in the analysis of microscopy images or similar
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conditions, to large field-scale experiments with wild and domestic grazers. These experiments will test hypotheses related to the effects on nutrient cycling of grazers with different body size and grazing
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models and neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio
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with large-scale data analytics frameworks (Hadoop, Spark, Flink, etc.) is desired - Interest in the development of software systems, very good knowledge and skills in programming with standard
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to design and conduct experiments at various scales from laboratory manipulations of animal plant-soil systems including micro and mesocosms under varying environmental conditions, to large field-scale