37 phd-studenship-in-computer-vision-and-machine-learning positions at SciLifeLab in Sweden
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-based methods to improve cancer therapy Your profile Qualifications PhD in bioinformatics, computer science, biology, medicine, or mathematical statistics. Experience in cancer research and analyses
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advanced biostatistics/machine learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a “user fee-based” support model. The projects will differ in
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maintenance of instruments and computer systems, as well as assisting researchers in preparation of EM samples, data collection, and image analysis. You will also act as a coordinator for ongoing facility
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maintenance of instruments and computer systems, as well as assisting researchers in preparation of EM samples, data collection, and image analysis. You will also act as a coordinator for ongoing facility
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, e.g git. Experience of Linux/UNIX-terminal. Excellent communication- and collaboration skills. You speak and write English fluently. Meriting Experience of HPC or corresponding computer systems
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course catalog and you will have the opportunity to participate in teaching at advanced level (PhD level and up). Requirements PhD in Bioinformatics, Computational Biology, Computer Science, or a related
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. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create
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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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DNA unit, you are part of a team that includes staff at Stockholm University, and is part of a research environment that includes the Human Evolution research program at Uppsala University and the
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to study host-microbiome interactions at the spatial level in the colon. The research activities of the doctoral student will focus on the experimental and computational analysis of spatial gene expression