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. Desirable Familiarity with supply chain management, operations, or organizational contexts. Experience with advanced statistical methods (e.g. multilevel modelling, causal inference, Bayesian methods
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work closely with the other PhD candidate of PAST, who creates high-resolution proxy-based reconstructions of the same paleoclimate. Together, you apply a Bayesian statistical framework to contrast and
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wet-dry cycles. Using state-of-the-art monitoring techniques you will track changes in permeability, particle mobilisation (turbidity), and volume and visualise flow pathways in real time. By
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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference
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programming techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference
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classical waves, forming a new regime of dispersive wave interactions. Thus, the nature of bandgaps in boundary layer flows remains unclear and unexplored. The main objective of this project is to
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estimation using multi-track Sentinel-1 (C-band) and NISAR (L-band) data. Implement and extend dynamic InSAR processing workflows for near-real-time analysis, including quality control and anomaly detection
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professor. The group has three research tracks: freeform design, imaging optics and improved direct methods; for more details see https://martijna.win.tue.nl/Optics/ . The following mathematical disciplines
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? Visit the page about working as a PhD candidate . A PhD track at Radboud University gives you room to follow your own interests and ambitions. Mustafa Akpinar PhD candidate at the Behavioural Science