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Field
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), consists of two main parts. First, the candidate will develop machine learning models aimed at improving the follow-up of neurocognitive function in critically ill children after discharge from the intensive
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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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; Midlands Graduate School Doctoral Training Partnership | Nottingham, England | United Kingdom | 26 days ago
ESRC DTP Strategic Joint Studentship University of Nottingham and University of Birmingham The Midlands Graduate School is an accredited Economic and Social Research Council (ESRC) Doctoral Training Partnership (DTP). One of 15 such partnerships in the UK, the Midlands Graduate School is a...
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is to develop a highly innovative ‘Lab on a Bench (LoB)’ setup, integrated with Machine Learning algorithm, as a high throughput method for screening and developing formulated products that are used in
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The Luxembourg Institute of Socio-Economic Research (LISER) is recruiting aPhD Candidate in Geospatial Data Science and Environment with a focus on Artificial Intelligence and Machine Learning (f/m
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FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics | Germany, | Germany | 2 months ago
5 Mar 2025 Job Information Organisation/Company FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics Department Department Mathematik Research Field Mathematics Researcher Profile
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Doctoral Network, we invite applications for up to two PhD positions working on challenging theoretical and practical problems in Safe Machine Learning. Specifically, we are looking for students to tackle
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operating point and environmental turbulent flow conditions. Understanding of all the former conditions is critical to inform industry about the performance of tidal turbines and to develop a machine learning
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will use the suite of machine learning tools that is readily available in Python packages. In other words, we will not focus on the development of new machine learning tools as everything we need is
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quantum chemistry (QM), machine learning (ML), and high-throughput experimentation (HTE). The objective is to develop a data-driven framework that enhances the efficiency and effectiveness of catalyst