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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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science. The successful candidate will develop innovative methods and models to decode the language of the genome and advance our understanding of how genetic variation contributes to complex diseases
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Referensnummer IFM-2026-00053 Work assignments This PhD position focuses on methodological and computational development in cryo-electron microscopy (cryo-EM), with emphasis on image reconstruction
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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, bioengineering, or related life sciences) Strong organizational skills and the ability to manage multiple tasks and projects in parallel Hands-on experience with experimental laboratory work (e.g. cell culture
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, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The incumbent
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Join SciLifeLab PULSE (Program for future leaders in Life Science) to move your research career forward. Why PULSE? Empowering Diversity in Science: PULSE is committed to fostering diversity and
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biology to pioneer research in immunology using single-cell and spatial transcriptomics data. The focus will be on development of novel computational methods for gaining fundamental insights into healthy
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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of cell nuclei We are actively seeking a highly motivated PhD student with a passion for developing experimental methods to join our team. In this role, you will be involved in a groundbreaking project