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contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models, alongside complex drug screening, efficacy
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and materials research that could not be addressed so far due to their high complexity, which prevents approaches that solely rely on classical mechanistic modeling or classical machine learning. Equal
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systems, focussing on the inclusion of thermal aspects. You can leverage a large body of existing research and software for the efficient simulation of complex geometrically nonlinear mechanical elastic
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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complex input. For instance, in physics-informed ML, in addition to data examples used by a standard ML setup, domain knowledge serves as an additional input. It can be in an explicit form of rigorous
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. Qualification and Experience: You have a degree (Diploma, Master's) in physics, nuclear physics, radiochemistry, or a related field. Experience in operating complex experimental setups and using data analysis
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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of Management is hiring a research assistant to support Finance, Accounting, and Economics projects. Working alongside another predoctoral assistant, the postholder will assist academics with data analysis
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on numerical aspects of the network model analysis. Being part of the wider Mathematical Neuroscience research theme within the School of Mathematical Sciences which currently includes 7 members of academic