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of reproducible code by using code sharing platforms, version control, workflow languages and container solutions is a merit. Experience in training deep learning-based models on HPC-resources is also meriting. A
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engineering practice, collaborative development experience (e.g. Git/GitHub), and ability to use AI-assisted coding tools effectively. Strong communication and collaboration skills, with the ability to work
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School of Engineering Sciences at KTH Project description Third-cycle subject: Biological Physics The Hannebelle Lab at the Division of Biophysics, Department of Applied Physics, studies
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, of which at least 60 second-cycle higher education credits, or acquired, in some other way within or outside the country, substantially equivalent knowledge. Degree in Mechanical engineering or similar from
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background in Machine Learning, with the ability to understand and extend current research Solid programming and engineering skills Comfortable working with modern development tools and practices Strong
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technical expertise with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab is a national resource hosted by Karolinska Institutet, KTH Royal Institute of Technology, Stockholm
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Job description Quantitative analysis of lipid nanoparticles (LNPs) using Cryo EM is challenging due to heterogenous
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computer science, image analysis and machine learning, engineering physics, data science, applied mathematics, molecular biotechnology engineering, or another related field; or Have completed at least 240
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technical expertise with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab is a national resource hosted by Karolinska Institutet, KTH Royal Institute of Technology, Stockholm
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KTH Royal Institute of Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health Job description Tissue organization is achieved by the genome encoding both cell type