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been directly mapped. This PhD position at Stockholm University focuses on leveraging machine learning (ML) to identify errors in large bathymetric datasets and applying ML techniques like "super
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) consolidator grant. Project description Project title: Pinpointing toxic chemicals from mass spectrometric data with supervised and unsupervised machine learning. Supervisors: Assoc. Prof. Anneli Kruve (main
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. In this project, we are recruiting additional Ph.D. student to leverage recent advances in machine learning to create better deep-learning models to predict protein-protein interactions and to apply
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between these. There will be a strong focus on developing machine learning tools and novel molecular representations. Fundamental knowledge of machine learning and programming as well as molecular biology
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such as dimensionality reduction, clustering and visualization in combination with advanced tools of machine learning and neural networks to build models of epigenetic regulation of gene expression during
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AI and mass spectrometry is essential. Candidates with experience with machine learning are encouraged to apply, while experiences in generative models and high resolution mass spectrometry
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culture, fluorescence microscopy, image analysis, mathematical modeling of dynamical systems, and machine learning is advantageous. Priority will be given to candidates with the overall highest experience
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approaches (QM/MM, molecular dynamics, free energy methods, machine learning). The PhD candidate will work closely with other PhD students, postdocs and senior scientists of the lab in an interdisciplinary
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approaches (QM/MM, molecular dynamics, free energy methods, machine learning). The PhD candidate will work closely with other PhD students, postdocs and senior scientists of the lab in an interdisciplinary
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seek a candidate with a keen interest in fundamental physics with technical aptitude in mathematical methods of theoretical physics, who is motivated to learn new skills and take part in developing new