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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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clinical and experimental datasets. You will join a collaborative environment and work closely with research groups specializing in microbiology (Avican Lab), machine learning (Erdem Lab), and network
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carried out in collaboration with all the project partners. You will work with X-ray tomography experts at the Division of Solid Mechanics and MAXIV plus machine learning experts at RISE to develop state
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the orientation of various types of nanoparticles within both thermoset and thermoplastic polymer matrices. This position entails close collaboration with industrial partners to implement the alignment technique
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, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially CRISPR screening, is highly meriting, as is experience with single-cell RNA sequencing or other omics assays
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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willing and able to learn quickly within a collaborative and interdisciplinary team. Instructions on how to apply For more information and documents/templates/europass link, please visit https
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proposal that will be further developed and contextualized in collaboration with the research leader in the research environment. Your research topic needs to be relevant for student learning in higher
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral