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resources. In this particular PhD-position you will have the opportunity to develop, optimize and validate advanced MR metabolic imaging methods, for 3T and 7 Tesla MRI, that provide quantitative maps
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Chair of Biological Imaging 02.02.2026, Academic staff We now seek a highly qualified and motivated PhD student (f/m/x) to drive the development of a novel quantum enhanced microscope. The Institute
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seek for the following capabilities: • PhD or Master in Biology, Biophysics, Biochemistry, Bioengineering or related topics. • In vivo imaging experience, preferably optical, ultrasound or even photo
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for PhD positions in plant science. The expected starting date is May 1st, 2026 or according to agreement. Umeå Plant Science Centre (UPSC) is one of the strongest research environments for basic plant
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 19 days ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional
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commencing on the 2nd of March 2026 (online or in person, depending on the candidate’s location). The selected candidate will need to follow the regular PhD application process at the University of Nottingham
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Your Job: At the Electrocatalysis department of Prof. Karl Mayrhofer, we offer a PhD position within the team Nanoanalysis of Electrochemical Processes. Lead by Dr. Andreas Hutzler, the team is
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PhD Studentship: Building Brain Circuits: decoding RNA processing in the developing mammalian cortex
in neuropsychiatric disorders, yet the underlying mechanisms remain poorly understood. This PhD project will investigate how an RNA processing enzyme controls the development of cortical circuits in
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engagement•Interest in translating scientific insights into policy and practice Selection process How to Apply Please submit: A motivation letter explaining your interest in this PhD and how your
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation