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Field
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. These methods will integrate machine learning techniques and real-time sensor data, to enhance operational efficiency, reduce costs, and ensure desired service levels, such as meeting a high percentage of demand
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multiple disciplines including Social Work, Social Policy and Administration, Psychology, and Sociology. Our research directions are related to migration and population mobility, family and life span
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for medical applications, micro-robotics, and materials science. PhD project description: The field of medical therapeutics has valuable applications for mobile devices for efficient drug delivery, called micro
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, including work with experimental in vitro and in vivo models (e.g., cell or animal models), as well as molecular biology techniques, is also considered an advantage. The ability and interest to work in
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material, and investigations will include, for example, characterizing novel patient derived cultures via various cell and molecular biological assays. Later, the modelling and therapeutical studies
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(ICS). A scientific environment of excellence, highly dynamic, where high-end biomedical projects are continuously developed. Continuous learning and a wide range of responsibilities within a stimulating
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-scale compound drivers. We will leverage machine learning methods to bridge the gap between drivers at coarse model resolutions and impacts captured by high-resolution observations. Job description Arctic
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secondments Salary Gross amount (per month) Living Allowance EUR 5470 Mobility Allowance EUR 710 Family Allowance EUR 660 GreenFieldData Project at glance : “IoRT Data management and analysis for Sustainable
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-disciplines; to promote and inspire discovery and learning that enhances patient care; and to contribute to the translation of scientific progress into clinical practice. The Department of Physiology consists
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in