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). Experience in mathematical modeling of DNA or protein evolution. Hands-on experience with model training (CNNs, transformers, …) and libraries (TensorFlow, PyTorch). Experience in software and method
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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University. As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and
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. The project focuses on developing novel representation learning and generative modeling methods to construct a unified cellular morphology state space across heterogeneous datasets. By leveraging shared
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with nano-LCs. Performing data analysis and interpretation using established proteomics software and tools, such as Proteome Discoverer, Spectronaut, Skyline, Byonic, Perseus and R-based Shiny
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protein analysis methods Preferred qualifications Experience from biotech/pharmaceutical industry or relevant academic research environment Experience with structural biology, especially cryo-EM Experience
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countries. Qualifications We seek an established scientific leader with strong experience in pandemic preparedness and laboratory infrastructure. The successful candidate should demonstrate: A PhD or
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and integrative (proteo-)omics expertise in the lab, guided by leading experts in terminomics, systems-level data analysis, and structural bioinformatics. Your profile A PhD in biology, biochemistry