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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 languages (e.g. Python) Basic knowledge of molecular biology, genetics and evolutionary biology Strong scientific curiosity, drive and independence Desirable knowledge and experience: Experience
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to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven
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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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programming and some wet-lab work. The doctoral position is part of the research school within DDLS (Data Driven Life Science). The doctoral student will primarily work together with their main and co
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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