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Field
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software engineering Solid Java/Javascript/Python programming skills. Solid mathematical background. Good background knowledge in the areas of business process modelling and model-driven engineering is a
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position The PhD student will: Develop machine learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch
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Leibniz Institut für Naturstoffforschung und Infektionsbiologie | Jena, Th ringen | Germany | 4 days ago
of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills LanguagesENGLISHLevelGood Research FieldBiological sciences » Biological engineeringChemistry
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, or ability to learn quickly) and/or symbolic/numerical computation (Mathematica, R, Python). • Fluency in scientific English (reading, writing, speaking) is essential to interact with the project's
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flow conditions. Here, thermodynamic consistency refers to a modelling framework in which the coupling between processes of different physical nature is derived from fundamental physical principles
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conditions. Here, thermodynamic consistency refers to a modelling framework in which the coupling between processes of different physical nature is derived from fundamental physical principles, ensuring
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theory and framework to study and explain how different reservoir systems work and how to design them for specific tasks. The project will combine: Mathematical modelling of dynamical systems
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within materials science and engineering. Use cases will be defined within different manufacturing techniques of lightweight structures to enable novel development of materials and process design. The PhD
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applications. The project is partially funded by AMD, and the successful candidate will collaborate with AMD researchers. As part of this research, you will: Investigate how different AI tasks perform on AMD
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learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch) using high-performance computing