33 phd-agent-based-modelling "NTNU Norwegian University of Science and Technology" PhD positions at SciLifeLab
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The doctoral student project and the duties of the doctoral student This Data Driven Life Sciences (DDLS) PhD project focuses on probabilistic models of protein structure, which can be used primarily
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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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hampers our ability to establish causal relations between molecular alterations and disease phenotypes. In this PhD you will address this by developing a deep learning model of cancer. The PhD position
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We are looking for a motivated PhD student to join us in a unique, collaborative project between our Affinity Ligand Design team at Cytiva and the Elofsson group at Stockholm University, where we
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and motivated PhD student to join an interdisciplinary project that combines computational biology, spatial transcriptomics, and tumor modeling to understand how the aggressive brain tumor glioblastoma
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. The student will work in a group addressing all these challenges, developing new AI-based methods to improve biological realism in simulations which will lead to more accurately inferred GRNs from real data
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! The research group The PhD student will be based both at AstraZeneca, Gothenburg, and at Chalmers University of Technology. At Chalmers, the student will be a part of the Holme Lab, based
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, computer science, computational biology and computational statistics. More information about us, please visit: Department of Mathematics . Project description We seek to recruit a PhD student for the following
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who will make major contributions to life-science research in Sweden. The advertised PhD position, based at Lund University, is generously supported through the DDLS PhD program. Description
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. The long-term goal is to enable targeted interventions for the right individuals, based on their lifestyle, disease trajectories, and molecular profiles. To achieve this, we will apply deep learning models