123 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation"-"St" Postdoctoral positions in Sweden
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Would you like to be part of our team and help shape the future of product- and production development? If so, you might be the one we’re looking for. The department of Product Development
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development and characterization of functional porous materials. Project description The project aims to develop new nanocomposites of cellulose and porous organic materials (POMs) with tailored structures and
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control technology and computer algorithms to develop a foundational discovery platform for future cell programming applications. This position involves both experimental and computational work
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revolution in the field of legged robotics and their successful custom deployment in various applications. Project overview The aim of this project is to develop a software framework for AI based Holistic Co
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an integrated development of network architectures, resource efficient algorithms, and programming paradigms for enabling an application-tailored design of dependable communication and computation systems
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build the sustainable companies and societies of the future. Subject description The research subject focuses on an integrated development of network architectures, resource efficient algorithms, and
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is to combine multi-gene control technology and computer algorithms to develop a foundational discovery platform for future cell programming applications. This position involves both experimental and
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in bioinformatics analysis and data visualization of our ongoing research projects on molecular characterization at the mRNA, miRNA, proteome and metabolome level from multiple lung compartments. Our
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and bioinformaticians to investigate the role of early life events in the development of early onset obstructive lung disease. Duties The overall project involves molecular characterization at the mRNA
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC