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of peptide design and chemistry, computational methods (machine learning, deep learning, genetic algorithms), microbiology, synthetic biology, and related areas essential to developing novel
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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the utility and the robustness of different explanation strategies. A large focus of this project will be on leveraging novel and interpretable approaches in applied domains such as algorithmic fairness and
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our software development team, developing novel scientific algorithms and applications in the areas of spectroscopic analysis and mining of the science data catalogues extracted from the pipelines
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organisational goals. Stay at the forefront of AI advancements, translating breakthroughs into actionable solutions. Develop robust algorithms and tools to analyse structured and unstructured data and improve
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skills and be interested in developing a collaborative program of applied research in robotics. For example, this may include sensor development, applied robotic perception, algorithm development, or other
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solutions using first motion. Experience in full waveform inversion using innovative tools (e.g. ISOLA) and methods (ML), earthquake location algorithms, computer programming and geophysical equipment