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Field
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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Description As part of a multi-disciplinary, integrated research team, the candidate will participate directly in efforts to relocate seismicity and to provide subsurface velocity models and geo
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scientists to develop and trouble-shoot new/complex protocols for implementation in RHS while participating in existing funded research. Candidates should have proven skills and expertise in analytical
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validating deep learning models for the prediction of disease progression from ophthalmic data. Skills include working with image or computer vision-based toolkits, development of multimodal, multidata type
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genomes. Dr Karam Teixeira's group uses Drosophila as a model to study transposable element biology, epigenetic regulation and inheritance, small RNA-based defence mechanisms, and germline biology in
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. Desirable Qualifications: Desirable Qualifications: Laboratory skills:in vitro and in vivo studies, flow cytometry, andtechniques in molecular biology Experience with mouse models. Competency in SolidWorks
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platforms, and an emphasis in deciphering complex protein-protein interaction networks that contribute to the pathogenesis of these diseases. This is a full-time, non-tenure-track position working in
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statistical methods for modelling and data treatment engage in teaching, innovation and advisory activities in relation to food technology, food chemistry, and food nutrition in a broad sense. Teaching
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In this position, you will work on microscopy-integrable measurement technologies used to study three-dimensional cell culture models of breast cancer tissues. This PhD candidate position is
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experience. Proven technical competence in experimental science and data analysis/modelling. Strong practical understanding in at least one of the following: medical device manufacturing, sustainable materials