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, with joint academic–industrial supervision Data-driven life science is a field of research that utilizes data, computational methods, and artificial intelligence to investigate biological systems and
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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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responsibility for laboratory operations and safety with active scientific engagement, for example through contributions to research projects, method development, and recruitment-related activities. You will work
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communicate on a daily basis with the Head of Unit and Lab Manager. You will also actively participate in technology development with regards to analytical methods and application of workflows to user projects
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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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methods. More about the department’s activities can be found at www.medsci.uu.se . The SNP&SEQ Technology Platform at IMV is part of the SciLifeLab National Genomics Infrastructure (NGI) in Uppsala. We
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Data Driven Life Science (DDLS). About the DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes
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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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experimentally challenging and we are using specially-developed methods for library prep for high-throughput sequencing to achieve this. The research involves next-generation sequence library preparation in