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opportunities to develop scientific independence, establish new research directions, collaborate across disciplines, mentor junior scientists, and acquire the skills and publication record necessary for the next
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information-security approvals required by the programme, and engage with internal University stakeholders (IT, HR, Teaching & Learning, faculties) and external partners. We welcome candidates who bring diverse
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companies writing scientific publications in peer-reviewed journals acquiring and implementing third-party funded projects. Other responsibilities include mentoring and supervising PhD students, Bachelor’s
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The successful candidate will join the Climate Physics team at ENS de Lyon (https://climatephysics-ensl.fr/ ), which currently consists of 5 permanent researchers, 7 PhD students and 2 postdoctoral researchers. We
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molecule interaction modeling, polymer physics, and statistical inference to investigate RNA structure, function, transport, and regulatory mechanisms in health and disease. AI and Machine Learning for RNA
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, room acoustic modelling, deep learning, acoustic
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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particular, in the areas of continuum mechanics of solids, computational mechanics and machine learning Willingness to independently familiarize oneself with new subject areas and work on interdisciplinary
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, permeability, and cytochrome P450 interactions would be advantageous; ▪ Knowledge and/or openness to learn of computer-aided drug design methods, in particular molecular docking, pharmacophore modelling, or in
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demonstrate: Essential Criteria: • PhD in Materials Science, Paper Science, Chemical Engineering, Chemistry, Polymer Science or a closely related discipline. • Experience in materials characterisation