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research and research-based teaching. You will teach and supervise students at Bachelor’s and Master’s level, and you will contribute to the development of the department through research of high
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genomic data science. Experience in single-cell and/or spatial genomic data analysis and machine learning methods is required. You are expected to have a proven track record of publishing scientific results
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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publication record relative to their career stage and a clear interest in interdisciplinary collaboration. Ideally, you also bring experience with machine-learning or hybrid modelling approaches, as
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academic or industry leadership roles. Your profile Applicants should hold a PhD in Computer Science, Electrical Engineering, Computer Engineering, Telecommunications, or a similar field, with a strong
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identification, and who have significant experience in applying Machine Learning (ML) and Artificial Intelligence (AI) to these areas. Applicants with theoretical, numerical, experimental, or combined research
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. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and process-based radiative transfer models
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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with troubleshooting their machines and support their understanding of core concepts. Guide students in working on their own project. Demonstrate best practices and foster the development
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Policy Implications and Recommendations Case Studies of Successful Innovation Funding Methods The project will employ a combination of methods, including machine learning (ML) and generative AI (GenAI