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University of Technology (TU/e)CountryNetherlandsCityEindhovenPostal Code5612 APStreetDe Rondom 70Geofield Where to apply Website https://www.academictransfer.com/en/342216/phd-position-in-causal-machine-learn
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Probabilistic Circuits. Causal Representation Learning. Causal Explanations. Causality and Large Language Models. Counterfactual learning. Job requirements Master’s degree in Computer Science, Mathematics, or a
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Irène Curie Fellowship No Department(s) Applied Physics and Science Education Reference number V34.7526 Job description Are you inspired by combining physics-based models with machine learning
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aspiring computer science researcher interested in deep neural networks? Do you want to understand the fundamental limits of machine learning? Then you have a part to play as a PhD candidate. By
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tool into clinical decision-making and pathways, explore alternative pathways, evaluate acceptance and expected usability of multiplex sensors and machine-learning-based decisions and explore alternative
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with your chip(s) will be analyzed with machine-learning algorithms. You will collaborate with researchers and companies of various disciplines like chemistry, embedded systems, software, signal
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curriculum; are familiar with the particular challenges that students may encounter while learning computer programming and computational data analysis (or are willing to learn more about this); are skilled in
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, machine learning, and large-scale data analytics. You will work closely with the advisors to define, develop, and execute your own research. The Ph.D. dissertation will be defined by you with inputs from
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. The data acquired with your chip(s) will be analyzed with machine-learning algorithms. You will collaborate with researchers and companies of various disciplines like chemistry, embedded systems, software
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machine learning, signal processing, epidemiology and causal inference, but all candidates with knowledge at the intersection of these three scientific disciplines are invited to apply. Candidates with