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an application for this position, you agree to the processing of personal data, in the meaning of Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection
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selected publications from your PhD thesis 2 recommendation letters Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR
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accumulate genetic and epigenetic changes that fuel somatic evolution through drift and selection. Over time, these processes turn tissues into molecular mosaics containing competing subclones, often carrying
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ICETA - Instituto de Ciências, Tecnologias e Agroambiente da Universidade do Porto | Portugal | about 9 hours ago
where the research leading to the candidate’s PhD degree was carried out must be different from the Associated Laboratory for Green Chemistry – Clean Technologies and Processes. If the PhD degree
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closely related field PhD training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years
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are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge engineering, linked data, web technologies. About the role
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, utilising implementation science methodology. The interventions will then be evaluated in a data-enabled clinical trial. This role is suitable for post-PhD researchers with experience in qualitative or mixed
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, we are looking for candidates to have the following skills and experience: Essential criteria PhD in relevant subject area * Experience in working with bioimaging and other spatial data, including
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Postdoctoral researcher (M/F). Modeling damage during earthquakes. Comparison with geophysical data.
. Use of digital earthquake models; The code used for calculations is written in Fortran, and the tools for processing input and output data are written in MATLAB and Python. The postdoctoral researcher's
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive