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at the Wallenberg Laboratory. The group is part of the national Data-Driven Life Science (DDLS) program, funded by the Knut and Alice Wallenberg Foundation. Their research focuses on developing computational methods
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KTH Royal Institute of Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health Project description Third-cycle subject: Medical Technology (Joint KTH-KI program) In
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development of phylogenetic methods. The EvonetsLab is supported by a Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven
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position is part of a five-year research program funded by the Wallenberg Foundation, which aims to develop and apply computational tools to understand the evolution of biodiversity (see https
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at the Wallenberg Laboratory. The group is part of the national Data-Driven Life Science (DDLS) program, funded by the Knut and Alice Wallenberg Foundation. Their research focuses on developing computational methods
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previous experience of the Spatial Transcriptomics method and data analysis as well as knowledge of the programming language R. The PhD student will be at KTH, Department of Gene Technology, SciLifeLab
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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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programming languages (e.g., Python, R). Experience working in a LINUX/UNIX environment. An excellent molecular biology skillset. Experience with NGS library preparation supported by a strong publication record
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publications in relevant research area. Candidates must be fluent in spoken and written English and highly motivated. Candidates are expected to pursue and plan research independently, but also to function in a
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degree in relevant fields (bioinformatics, immunology, computational biology, mathematics, and/or statistics). Strong programming skills in R and/or Python Demonstrated strong ability in analyzing high