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, or similar, and a degree in Environmental Engineering, Environmental Science, or a related quantitative field. Position 2 will focus on large-scale data analytics and machine learning. Applicants should have
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trustworthy machine learning, with a particular emphasis on mechanistic interpretability and its application to healthcare data. The successful candidate will contribute to understanding how modern machine
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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. The opportunity: We are looking for someone to analyse environmental soundscapes using signal processing or machine learning. Initially, you will work with underwater passive acoustic sound data, and later may
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to leverage machine learning approaches for the optimization of polymer properties and degradation profiles. The successful candidate will lead pioneering research in controlled polymer synthesis, employing
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asynchronous AI-led chemical optimisation across chemistry laboratories¿. This role sits at the intersection of robotics, machine learning, and chemistry, aiming to develop robotic systems that work
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science. Familiarity with ship data processing, ship performance analysis, machine learning algorithms (Deep learning, Reinforcement learning, etc.). Proficiency in written and spoken English - essential for data
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep learning, with experience in time series forecasting or related