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proofreading, annotation and analysis of connectomics data. This will include whole-brain scale analysis of multimodal features such as connectivity, morphology or cell ultrastructure. Of particular interest
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postdoctoral position in data analysis, where you will apply machine learning techniques to understand how resistance genes spread and to help detect infections caused by resistant bacteria. The position is part
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connectome, with a focus on the chemosensory circuits involved in human host-seeking. The principal focus will be on the high level proofreading, annotation and analysis of connectomics data. This will include
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IUT, in Diophantine geometry, in applications of modern mathematics to deep neural networks, and related areas. The previous research work has been partially supported by research grants from several
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working in radiological environment. Experience in heat transfer and thermal modeling/simulation using finite-element analysis (FEA) or other software. Ability to work within a multi-disciplinary team
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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Medical Physics in Radiation Oncology is seeking, in close cooperation with the Division of Radiooncology/Radiobiology
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(https://www.naritalab.com/ ) seeks a skilled Research Assistant for a 3-year fixed-term role starting October 2025. The Narita Laboratory investigates the interplay between cellular senescence and tumour
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satisfactory performance and funding availability. Applicants should possess a Ph.D. degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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, and wireless communications Proven track record of publications in relevant IEEE journals and conferences Extensive experience in physical layer algorithm design and performance analysis In
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in physical layer algorithm design and performance analysis Proven track record of publications in relevant IEEE journals and conferences In addition to the above qualifications, it is highly desirable