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Machine Learning within the School of Medicine at the University of Limerick. This is a methodologically focused PhD for candidates with strong quantitative backgrounds who wish to develop novel statistical
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CoE (https://www.med.uio.no/hth/english/ ) is inviting applications for a three‑year PhD position. The recruited candidate will take part in UiO Emerging Technologies Initiative Project FTI-NAM. NAM
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, United States) demonstrated net en-ergy gain in inertial fusion for the first time, marking a major scientific advancement. This breakthrough has since spurred numerous international projects aimed at developing
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student-scholarship holder at the Poznan Doctoral School of Institutes of Polish Academy of Sciences
this process promotes metastasis. The results obtained may contribute to the identification of new prognostic markers and potential therapeutic targets, supporting the development of more effective treatments
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: The PhD Project: The successful applicant will develop an original doctoral project within the broader research agenda of Bodies of Knowledge. Applicants are invited to propose an empirical case study of a
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technological development. As a PhD student in this project, you will operate and evaluate different laboratory-scale biogas processes with the aim of optimising both biogas production and biofertiliser quality
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closely together with mass spectrometrists, glycobiologists, and computational experts as part of an established R&D team that will train and support you in your project. Where to apply Website https
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of transfer-printable tunable narrow-linewidth laser sources for interferometric near-infrared spectroscopy (iNIRS). This research sits within CARE 2, which aims to develop a wearable biomarker sensing
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proton transfer. For the Diffusion Processes Group we are looking for a PhD Student Your tasks You will prepare and characterize clay samples and oriented clay films using different methods (e.g. XRD
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their physical efficiency through training, this project aims to develop software methods that allow deployed neural networks to progressively reduce their resource footprint while maintaining application