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for statistical computing and data visualization Deep learning frameworks, such as PyTorch or Tensorflow and data science tools such as Numpy, Pandas and Matplotlib Experience in machine learning management systems
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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral
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implementing AI/ML methods (e.g., machine learning, deep learning) for life science research. Collaborating with research groups to identify needs and opportunities for AI/ML support in their projects
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on histopathology image data. As a postdoctoral researcher you will be involved with development and validation of AI/deep learning solutions for precision medicine and patient stratification (patient outcome
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are robust, especially in the selected industry use cases. This staff scientist position is linked to the research group Deep Data Mining in the department of Computing Science, which focuses on fusing data
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and development of algorithms, methods, and theories aimed at better understanding the properties and underlying mechanisms within statistical and deep learning-based systems also in the presence
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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functional neuroanatomy with a focus on increasing the spatial precision of treatment methods such as deep brain stimulation (DBS) and discovering the underlying neurobiological mechanisms in neurodegenerative
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will find yourself in a team that values creativity and allows you to influence the decisions made within the group. Furthermore, we value continuous learning and encourage you to allocate time for