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/relevant work experience. Applicants for the Research Assistant post should have an honours degree, preferably in Mathematics or an equivalent qualification. Applicants are invited to contact Prof. Xiao-qi
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should have an honours degree or an equivalent qualification. Applicants should also have two or more years of experience in laboratory diagnosis of tuberculosis. Applicants are invited to contact Prof
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independently as well as in a team. Preference will be given to those with experience in cardiology, inflammation and metabolic research. Applicants are invited to contact Prof. Cai Yin at telephone number 3400
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course that is part of the educational project of a higher education institution, developed in association or cooperation with one or more R&D units — this requirement must be duly verified at the time of
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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, and PhD and project students. This is on a part-time basis to enable other career development commitments. About you You will have a clinical qualification with current GMC registration, along with
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Research Fellow (Fusion Shielding Materials) - School of Metallurgy and Materials - 105638 - Grade 7
variety of research methods, such as scientific experimentation, literature reviews. Analyse and interpret data Contribute to developing new models, techniques and methods Disseminating research results
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) Prototype evaluation and model optimisation . WORKPLACE AND SCIENTIFIC SUPERVISION: The work will be developed at the CDRSP, under the scientific guidance of Prof. João Manuel Matias. . GRANT FINANTIAL
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. The application areas are diverse, ranging from quantum devices to medical technologies. Overall, modeling and simulation tools developed in Computational Physics have increasing role in fundamental sciences, and
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and