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Field
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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prostate cancer risk across diverse ethnic groups. This work aims to support more equitable risk stratification in cancer screening programmes. Using simulations based on multistate modelling framework
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modelling and density functional theory calculations will be used to further increase our understanding of the photo-reduction mechanism. Correlating these theoretical insights with the structure and activity
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Gallium Nitride: covering the entire chain from device modelling and
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process is poorly recorded and needs improvement. Aims and Objectives In collaboration with the Health Innovation Partnership, a modelling pipeline will be devised to cope with the challenges of data
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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join a vibrant, supportive research community (around 20-25 people involved in fluids modelling research). Collaborate with the Leonardo Centre for Tribology: Work with top researchers on experimental
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are desirable, e.g., regression analyses, repeated-measures analysis, structural equation modelling, visualisation, preferably in R • Competences in quantitative research methods - ideally knowledge of several
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 17 days ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using