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the complex food composition, which can lead to masking effects and molecular interactions in the food matrix. The applicant will work on the development of machine learning prediction models
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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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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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, 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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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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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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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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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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, 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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spatial database management, in data governance and the establishment of user and linked data services. Competencies in the fields of data literacy, spatial data science, GeoAI, machine learning and