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Field
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cohort of doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation
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processes. • Apply machine learning techniques and advanced statistical analysis to extract knowledge from complex datasets. • Participate in the evaluation and optimisation of high-performance scientific
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uniting expertise from (i) machine learning, statistics, mathematics and data science, (ii) law, and social sciences, and (iii) philosophy, the centre establishes a new framework and approach for advancing
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, clinical data and AI-driven modelling for cancer research! In this role, you will bridge the gap between machine learning, computational biology, and haematological oncology. You do not need to arrive as an
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AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics, machine learning, and photonics. Be part of a multidisciplinary research team spanning
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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-of-the-art machine learning methods, acoustic sensing can provide valuable insight into a wide range of processes occurring within the built environment. Potential applications include structural health
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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feedback modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. Description of the SPARK4B+ project The position is within the EU