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, or landscape modelling Further, we will prefer candidates with some of the following qualifications: Teaching and supervision experience at the BSc and MSc level Interest and preferably experience in developing
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contribute to development of research grant applications. Your profile The applicants should hold a PhD in structural dynamics with focus on data-driven methods (e.g., for input/state/parameter estimation) and
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. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
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University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as
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decade HLA class II antigen presentation has been accurately described and methods developed that predict this event with a high level of confidence. In comparison, a detailed understanding of the rules
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University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as
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on the development of anti-infective drugs and vaccines using the pig as a model platform. Main tasks will include the design, coordination, and execution of pig experiments, and assessment of drug and vaccine
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immediate biomedical relevance Access state-of-the-art labs and infrastructure at DTU Health Tech Collaborate with an interdisciplinary team of chemists, engineers and biologists Develop your academic career
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Two year postdoc position at Aarhus University for single molecule FRET based investigations of l...
concerns an integrative effort where several cryo-EM structures are used to develop donor and acceptor labelled proteins and complexes to follow their large scale rearrangements by single-molecule
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with