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learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch) using high-performance computing
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processing, computer vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. The University may permit
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R and/or Python, with experience in data integration and statistical analysis. Exposure to RNA therapeutics or functional genomics approaches is an advantage. Strong interest in interdisciplinary
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, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with programming languages (Matlab, Python, R) and geospatial tools (e.g. ArcGIS
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, biomedicine, biotechnology, or another relevant field. Documented experience in programming or quantitative data analysis, for example in Python, MATLAB, or similar environments. A strong interest in method
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skills, particularly in Python and/or R · Experience in machine learning and AI · Fluency in spoken and written English · Ability to work independently as well as in interdisciplinary teams Meritorious
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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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. Our software is built primarily in Rust, with R and Python parts to support bioinformaticians. Our web frontend is also developed in Rust (web assembly) – all to support fast computing and handling
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(see below) are also encouraged to apply. Proficient in at least one programming language, preferably Python or R. Experience in any of the following areas: large scale sequence analysis, microbial
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for seeking the position. Extensive experience with Python, Matlab, and R, and good UNIX knowledge are essential skills, as well as familiarity with biological omics data analysis techniques. Experience with