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) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures
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to alter and characterize the 3D cell cultures including their microenvironment. For evaluation you will use a combination of computational and experimental techniques, including e.g. numerical modelling
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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for mathematics research and education. The Computational Biology and Biostatistics groups develop mathematical models to address biological phenomena like systems biology, cancer modeling, statistical genetics
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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documented international research experience, a demonstrated potential for creativity and a high degree of excellence in the field, for example in computational biology, mathematical modelling and simulations
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by using computational models of development to simulate the evolution of evolvability. Main responsibilities The main tasks include: Large-scale simulations of development and evolution under
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and experiences. We regard gender equality and diversity as a strength and an asset. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-yr initiative funded with
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research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in digital image processing and deep learning
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diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study