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Field
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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
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shape most of the cosmos we observe today. Many of the most compelling models in these areas predict stochastic backgrounds of gravitational waves—signals that may be detected by current and next
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with coalescent theory from population genetics. The central idea is to replace heuristic discrete denoising schemes with coalescent-inspired stochastic processes, leveraging the deep duality between
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of the cytotoxic drug to kill tumour cells will be included to simulate the response of the tumour to ADC treatment. Stochastic or agent-based approaches will be used to describe the heterogeneity of antigen
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-relevant properties such as thermal stability, controlled stochasticity, switching dynamics and compatibility with neuromorphic architectures. The goal is to build and validate an automated multiscale
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), Computational Biology (stochastic and analytical models of gene expression), Signal Processing (machine learning, image and signal processing), Biophysics, Microbiology and Single-cell Biology (flow cytometry
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with coalescent theory from population genetics. The central idea is to replace heuristic discrete denoising schemes with coalescent-inspired stochastic processes, leveraging the deep duality between
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under uncertainty”. This position is part of the ERC-funded DECIDE project lead by Professor David Pisinger. You will be responsible for developing new methods for decision support in stochastic
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A new PhD fellowship in path signatures, stochastic analysis, and
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knowledge of mathematics (e.g. linear algebra, probability, statistics, stochastic processes) Experience of developing in larger projects While we don’t expect applicants to know biology, applicants