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applicants who demonstrate substantial hands-on experience implementing, training and evaluating deep-learning models will be considered. General interest in AI or experience limited to running tutorials
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various levels of expertise. Education Requirement: A PhD or MD/PhD degree in Health Sciences or a related field. Required Qualifications: Expertise in cancer biology, with at least 2 years of experience
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doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia. Your immediate leader will be the head of
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the performance of the welded interface. Implement the developed models in commercial FE simulation software for the development of a digital twin and validate their accuracy against experiments. We are looking
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professional or research experience, deemed equivalent by the Graduate Research Committee (or delegate). How to apply We are seeking expressions of interest from qualified domestic candidates who wish to apply
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instruments at our local core facility, with comprehensive cryo-EM training provided — no prior cryo-EM experience required. As part of your PhD training, you will gain hands-on experience with advanced
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academic background, you may be considered if you can document that you are exceptionally suited for a PhD education, for example through relevant work experience and/or peer-reviewed academic works. You
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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
through relevant work experience and/or peer-reviewed academic works. Good oral and written presentation skills in English. The appointment is to be made in accordance with NTNUs guidelines for recruitment
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PhD, you will: perform large-scale potato experiments using 100 contrasting soil microbiomes; use automated high-throughput phenotyping to quantify plant growth, physiology and disease development
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geological field data. You will also design and run high-pressure shear-cell experiments on natural and analogue granular materials at Chengdu University of Technology, develop discrete-element simulations