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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
. Experimental validation of the established AI model is also expected to be a part of this project. Specifically, your tasks will be: Running numerical simulations to generate training data Building transformer
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Research Training Programme, for courses at other local scientific institutions of higher education, and at other universities or external institutions. Students are also expected to work independently, e.g
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well as to initiate new queries. As a member of an interdisciplinary team, the candidate will have the opportunity to receive training in a variety of techniques, including molecular biology, embryology, imaging
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-approved systems. Ensure adherence to data security and compliance protocols. Preferred Qualifications MSc or PhD in Bioinformatics, Computational Biology, Computer Science, or a related field. Strong
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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, and eager to apply computational skills to cutting-edge biological questions. In this project, you will develop a tool to infer karyotypes from individual cells based on their transcriptome, and use
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. The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental
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the body’s capacity for oxygen transport. Additionally, iron plays a vital role in energy metabolism, neurological function and immune health, all of which are essential for training and performance
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Job Description Are you passionate about metabolic engineering of bacteria? Do you want to contribute to solving urgent problems with synthetic biology? The Systems Environmental Microbiology (SEM
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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting