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(Stockholm, Sweden) and the candidate will benefit from a strong (inter-)national network of collaborators in protease biology and computational proteomics. The successful candidate for this position will join
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according to KTH’s Doctoral student salary agreement . Read more about Doctoral studies (PhD) | KTH | Sweden . Union representatives Contact information for union representatives. Doctoral Student’s network
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-driven analysis. The PhD student will be part of the DDLS Research School, a national program offering courses, workshops, and networking across Sweden. The project is conducted in collaboration with KTH
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identification methods Documented experience with deep learning for biological sequence or image data (transformer architectures, vision transformers, graph convolutional networks) Documented experience with multi
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. The research environment is highly collaborative and interdisciplinary, with close links to national and international networks and consortia. The Data-Driven Life Science Research School Data-driven life
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. Experience in deep learning, computer vision, or neural network development. Experience with live-cell microscopy, fluorescence microscopy, or analysis of 3D/4D image data. Experience in cell biological
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principles and approaches for managing and sharing different types of research data, as well as being engaged in competence-raising networking within research data management in Sweden. To perform the work
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more about Doctoral studies (PhD) | KTH | Sweden . Union representatives Contact information for union representatives. Doctoral Student’s network (Students’ union on KTH Royal Institute of Technology
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. Location: SciLifeLab , Solna https://ki.se/en/research/research-areas-centres-and-networks/research-groups/uncovering-the-molecular-and-physical-principles-governing-early-embryonic-division-and-nuclear
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, the following are required: – Documented several years of experience in training, evaluating, and deploying machine learning models, including deep neural networks and relevant frameworks – Documented several