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samples and disease models. Working closely with a dynamic and multidisciplinary team of clinicians and scientists, you will help generate and interpret high-resolution datasets that reveal new insights
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including experts in epigenetics, computational biology, and embryo modeling. Undergo postdoctoral training and career development (publishing, funding, leadership, networking, job search). PhD in Life
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Bring segregation theory and models to industrial practise. Job
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later than 1 March 2026. The position is a full-time position. You can read more about career paths at DTU here . Further information Further information may be obtained from Professor Adriano Sciacovelli
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targets. You will hold a relevant PhD/DPhil, together with sufficient specialist knowledge in normal and malignant hematopoiesis research. Experience generating genomics data from leukaemia samples with
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) detection, we will make AI for medical imaging transparent, robust and data-efficient. Position Overview As a postdoctoral researcher, you will forge next-generation explainable medical foundation models. You
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testing and stakeholder engagement. Collaborate closely with the PhD researcher to connect environmental data analysis with computational design innovation. Participate in fieldwork in Norwegian glacier
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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://as.nyu.edu/biology/directory.fei-li.html This position is for a post-PhD trainee preparing for a research scientist career path. The planned position will provide a transition to career independence through
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master's degree (or equivalent) and PhD in Mathematics/Statistics/Data Science Research expertise in the analysis of complex systems Familiar with network analysis, concepts of resilience (e.g. adaptive