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in multimodal imaging. Experience in machine learning is highly valued. You will support user-driven research projects and develop integrated data workflows spanning light microscopy (confocal, super
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allocation proposals, conducting machine learning workflows, and developing complete models. Example applications include microscopy image data, cryo-electron microscopy, structural prediction and dynamic
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data types (transcriptomics, proteomics, imaging). AI/ML Applications: Applying machine learning or AI to predict gene function or discover functional relationships from perturbation data. FAIR
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in artificial intelligence (AI) to join our growing biomedical innovation team. In this pivotal role, you will lead and contribute to the design, development, and deployment of machine learning
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experience with machine learning techniques in general and neural networks in particular will be highly beneficial. About the employment The employment is a temporary position of 2 years according to central
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data types (transcriptomics, proteomics, imaging). Knowledge on AlphaFold for models in structural protein analysis/proteomics AI/ML Applications: Applying machine learning or AI to predict gene function
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dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You
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of the proteins involved in the project, but also applying machine learning to predict the effects of allosteric modulation and to understand the biology of the specific systems we are studying. Qualification
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related fields. Experience in Machine Learning/AI, mathematical, computational and statistical training are also advantageous. About the employment The employment is a temporary position of two years
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, and manipulation of high-dimensional imaging and mass spectrometry data Experience in designing and maintaining reproducible and scalable analysis workflows Solid foundation in statistics and machine