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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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focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop
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modeling, machine learning, and AI techniques applied to biomedical data is a plus. Clinical Proteomics: Experience with clinical trial data, real-world evidence (RWE), and biomarker-driven trial designs is
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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at Sahlgrenska Academy of relevance include genomics, metagenomics, culturomics, proteomics, transcriptomics, software development, machine learning, and other statistical analyses of large-scale health data
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. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in time and space, how this affects
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
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in multimodal imaging. Experience in machine learning is highly valued. You will support user-driven research projects and develop integrated data workflows spanning light microscopy (confocal, super
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data. Much focus is on large scale analysis based on machine learning, deep learning/AI, as well as handling and analyzing large 3D microscopy data. You will work with shorter and longer projects and
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Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently Teaching and mentorship abilities or interests in personal development A