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at TU Delft. In this project we also work together with experimental groups at TU Delft and beyond. The Delft Bioinformatics Lab has strong algorithmic and machine learning expertise, with a profound
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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the attractiveness to the users, we need innovative designs where fixed and flexible services support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and
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without neurons in physical systems, Ann Rev Cond Matt Phys14, 417 (2023) [4] Dillavou, Beyer, Stern, Liu, Miskin and Durian, Machine learning without a processor: Emergent learning in a nonlinear analog
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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Learning Centre; A complete educational program for PhD students; Multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses; 7 weeks
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you have a background in deep learning and computer vision? Are you
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning