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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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running code to process sequencing data on a computer server, applying statistical models and machine learning algorithms, and performing downstream data analysis, including statistics and visualization, as
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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develop experimental and computational methods to identify molecular mechanisms leading to dysfunctional cellular states in human disease (www.camunaslab.org ). The project will require significant
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interpretable models that connect molecular patterns across multiple scales of human biology. The project lies at the interface of artificial intelligence, computational biology, systems medicine, and precision
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computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP
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Collaboration Conduct research together with academic, industrial, and healthcare partners in a highly collaborative environment. Who we are looking for A highly motivated PhD candidate excited by computational
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experimental and computational methods to identify molecular mechanisms leading to dysfunctional cellular states in human disease (www.camunaslab.org ). The candidate will contribute to the improve Slice-seq, a
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, animals, fungi, bacteria, etc.). The Program for Plant Ecology and Evolution, where this position is located, is home to eight research group focusing on genomics, adaptation and speciation as well