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the national Data-Driven Life Science (DDLS) program. About the position and the project As an industrial PhD student, you will be employed by the startup company PredictMe AB while being formally enrolled as a
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devote oneself to a research project under supervision of experienced researchers and following an individual study plan. A doctoral degree corresponds to four years of full-time study. This is an industry
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analysis, and biology, as well as Python and R programming language (critical). Previous experience analyzing high-resolution spatial VDJ/transcriptomics, long-read sequencing, single-cell transcriptomics
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discipline Experience in R programming or other relevant statistical software Excellent written and spoken English communication skills (proficiency in Swedish is not required) Additional merits: Previous
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programming language, preferably Python or R. Experience in any of the following areas: large scale sequence analysis, microbial genomics, human gut microbiota research (shotgun metagenomics), Metagenome
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methodological innovation as well as real biomedical applications. Applicants should include a personal letter and CV with information about programming skills. Eligibility requirements The position follows
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for the project. Have documented programming experience in R, Python or other common programming languages. Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning
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structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data- Driven Life Science (DDLS) aims to recruit and train the next generation of
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the time the applicant is admitted to the PhD program at the Department of Biochemistry and Biophysics. You meet general entry requirements if you have completed a second-cycle degree, or completed courses
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cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life