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will have a strong background in socio-technical threats and attacks, deception and social engineering, and security and cybersecurity metrics analysis (qualitative and quantitative). With a high
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-regulatory network analysis approaches, developed and employed in the Malysheva Lab, are required to reveal these mechanisms. With the proposed project we aim to bridge this gap in our knowledge of cis
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experience with scientific computing, data analysis, machine learning and/or AI You have an interest in environmental sustainability and pharmaceutical production Considered a plus: You have experience with
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appointment) in digital text analysis, digital humanities, linguistics and literature, applied linguistics, communication sciences, computer sciences or similar. You have a background and/or strong interest in
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on cancer metastasis and novel metabolic pathways. We exploit mouse models, genetic engineering, metabolomics and single cell & spatial multi-omics analysis to gain groundbreaking insights into metabolism as
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critical, scientific mindset and are interested in proteomics technology and data analysis. You have basic knowledge of statistical tools. What we offer We offer a full-time PhD scholarship for a period of 1
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, and material failure, with the support of a fully funded project. You will work on a cutting-edge research project focusing on investigating the interplay between mechanical stress and environmental
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experimental sites • coordinate and perform soil health analysis across all experimental sites • derive cause-effect relationships between drivers (regenerative practices), mediators (soil organisms) and impacts
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international interdisciplinary research team focused on computer vision and surgical video analysis, we are looking for a motivated research assistant. This is a full-time, one-year position that is renewable
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analysis or machine learning (ideally, we will recruit one candidate with specific specialization in machine learning and one student with specific specialization in statistics and data analysis; familiarity