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Field
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architectures, graph neural networks, representation learning, or large-scale training Strong mathematical and algorithmic background, with the ability to design and analyse novel machine-learning
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bioinformatic and analytic aspects of predictive modeling. MINIMUM JOB QUALIFICATIONS: A Ph.D. in bioinformatics, genetics, statistics, mathematical, physical, or computer science, or comparable research
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field, or be in the final stages of PhD submission. Strong scientific programming skills in Python. Knowledge of mathematical models of biological systems and numerical methods for differential
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. Potential applications include mathematical biology, superconductivity and nonlinear optics. The position offers opportunities for international collaboration and for presenting research at major national
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skills Documented ability to conduct high-quality independent research. Strong computational, mathematical, or modelling skills relevant to complex mobility systems, including the ability to
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into mathematical models, implementing them into robust engineering and scientific software tools, simulating complex engineering systems, and conducting techno-economic evaluations. The position
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activities in areas such as numerical analysis, mathematical modelling, development and analysis of algorithms, machine learning, optimization, scientific software development and high-performance computing
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degree corresponds to the profile of the post. Documented knowledge of statistical theory and methods and core machine learning methods. Documented Python programming skills. Excellent skills in written
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, chemistry, and mathematical modelling. The long-term goal is to derive design principles for constructing smart, adaptive microsystems, potentially useful for targeted drug delivery, cargo transport
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, including the School of Mathematical and Natural Sciences, the Global Security Initiative, and other units engaged in cybersecurity, computer science, artificial intelligence, and digital investigations