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on best practices and current developments in areas such as research software engineering, research data management, machine learning in the Leibniz Association, as well as at national and
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, microscopy and computer tomography. The scientific work focuses on the use of renewable and recycled raw materials in high-performance FRP and the tracking of FRP cycles. Scientific work on research projects
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expertise in computational materials science, density functional theory, machine learning, etc. Ability to work within an interdisciplinary team of scientists, engineers, and students. Excellent knowledge
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researchers from different fields, including biologists, epidemiologists and clinicians Proficiency in statistical programming with R and/or Python Prior experience in Machine Learning or Computational Biology
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knowledge in the field of machine learning, in particular deep neural networks Practical experience in the application of machine learning algorithms for both regression and classification problems Excellent
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, these information systems address the needs and interests of the various target groups. Data assimilation and aggregation is done using machine learning and modeling. Within the project, we are looking for a Student
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candidate to join our team to contribute to a project which will focus on the development of machine learning (ML) architectures for predicting and designing enzymatic reactions. To create the ML framework
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Data, geodata) Data visualization, georeferencing and web mapping (e.g. D3.js, open source GIS, R, Shiny)Programming, testing and documentation (e.g. Python, R, Jupyter, git) Machine learning and
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results Motivation to learn and communicate about Planetary Boundary Science Analytical interest in and understanding of political and scientific debates Project management skills and experience in
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, survey) or applied microeconometrics, and applied economics. You have experience with big data and machine learning methods? This would be a particular asset! With excellent English language skills, both