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for the entire project from polymer synthesis, crystallisation-driven self-assembly, characterising data collection interpretation. Training will be provided in all of the component techniques but demonstrable
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focus of the position is on application and advancement of modern artificial intelligence (AI) methods in drug discovery and development. An ideal candidate will have strong background in machine learning
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. Research experience after completing the doctoral degree is expected. Scientific competence in developing artificial intelligence methods, with proven publication record in scientific venues in the field, as
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seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake
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methods Undertake management/administration arising from research Contribute to Departmental/School research-related activities and research-related administration Contribute to enterprise, business
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Supervise students on research-related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new computational models, techniques and methods Undertake
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electrophysiology: Proficiency in whole-cell patch clamp recording, acute slice preparation, and/or cell culture electrophysiology, along with related data analysis methods using tools such as MATLAB or Python
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technical expertise but must also contain a well-defined training element and a qualification plan must be established for the scholarship period. Biostatistical competence and experience in working with