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position The PhD student will: Develop machine learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch
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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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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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, excellent study results, high proficiency in programming (preferably in Python), good communication skills with sufficient proficiency in oral and written English, personal characteristics such as a high
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strong interest in developing new theory and methods for signal processing and machine learning strong mathematical background good proficiency in programming (preferably in Python) good oral and written
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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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information here: https://ki.se/en/research/research-areas-centres-and-networks/research-groups/molecular-epidemiology-of-aging-sara-haggs-research-group A key strength of MEB is its strong collaborative
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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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(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