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Field
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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Designing hierarchical graph‑based models to predict toxicity under uncertainty by linking molecular‑level and system‑level knowledge Advancing causal inference methods to predict transformation products
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for therapeutic intervention. This PhD project will leverage large-scale single-cell RNA-seq and spatial transcriptomics datasets from infection biology to develop models, including transformer-/graph-based models
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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pangenome graphs, and identify trait-associated structural variants. Moreover, we have developed imputation methods that provide accurate genotypes in pedigreed populations, and haplotype-based association
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, Circulab at SU will be used extensively for accelerated materials design, characterisation and discovery. Data sets and multiscale modelling that spans over atomic structures, molecular graphs
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for therapeutic intervention. This PhD project will leverage large-scale single-cell RNA-seq and spatial transcriptomics datasets from infection biology to develop models, including transformer-/graph-based models
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Optimization: Mathematical Phylogenetics During this project, you will work on fundamental graph-theoretic and algorithmic problems in mathematical phylogenetics. Job description The Discrete Mathematics and
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PhD Studentship: Bottom-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution
misfolding-related diseases such as Alzheimer's - but predicting how proteins fold in biological environments remains a key unmet challenge. This project brings together insights from efficient graph-driven
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computing and decentralized intelligence where a swarm of nodes learns graph dependencies by effectively integrating the structure of distributed systems into neural network architecture. This approach