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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical
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of the infrastructure. We envision that you will start with the easy assignments and then, as you learn and become more experienced, progress to increasingly difficult/qualified work. Qualifications The requirements
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data at an internationally competitive level. Experience of biostatistics or machine learning approaches Proficiency in a scripting language like R or Python, as well as ability to work efficiently in a
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-related research experience in multi-omics data integration and statistical modelling familiarity with machine learning methods for biological data experience in data visualization or development of user
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral