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to capture the spatial complexity of tumor organization and its relationship to treatment response. This PhD project aims to develop robust multimodal predictive models of platinum resistance using a large
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of immunoglobulin production shape the survival of normal and malignant plasma cells. Using in vitro plasma cell differentiation systems, mouse models (9), and advanced molecular and cellular technologies (10), we
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, prior distributions and posterior predictive checks, model comparison, programming in R (python/Matlab), implementations using R-packages rstan/JAGS and brms/STAN or equivalent interfaces. References
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mining challenges. The overarching objective of this project is to develop computational models that can predict how effectively glycine-based solutions extract precious metals from ore, enabling
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: Implementation and fine-tuning of antibody design models (RFdiffusion and boltzgen, AlphaFold3 etc.). Implementation of affinity prediction and maturation (FoldX, RosettaFold, ESM etc.), virtual screening and
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required for the project or the hosting universities. This full-time 3 year PhD studentship focuses on the use of technology to assess symptoms of PD and for PD prediction. The key aim of this PhD is to
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will use a large dataset of P. aeruginosa genomes and experimental metadata to predict key mutations to the organism. The postdoctoral researcher will join the Whelan lab led by Dr. Fiona Whelan
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test coupons used in HiSOPE RF/microwave PCB & interconnects: Layout controlled‑impedance CPW/microstrip transitions from drivers to OLED fixtures; model launch structures, vias, and ground‑reference
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plus. Experience with advanced analytics, including predictive modeling, data science, or statistical analysis to support data-driven decision-making. Demonstrated experience designing and implementing
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, assess the health state of systems, and predict their future evolution and remaining useful life. The proposed approach integrates physics-based and data-driven modeling techniques, including machine