219 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" PhD positions in Netherlands
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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) and follow courses at the Graduate School of Life Sciences and/or Philosophy; you will also teach Bioethics and Medical Humanities courses at the Faculty of Health Sciences. Where to apply Website https
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doing their final assignment on sub-projects pertaining to the current research project. The PhD research will be performed in the Production Technology group. Where to apply Website https
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quantitative methodological skills in handling detailed spatial data, including various econometric techniques and machine learning approaches; a thorough understanding of empirical, explanatory research; a
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the response model from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques
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of related events and activities. In addition, they may be given a temporary appointment at the University to design and teach an undergraduate course in the general area of their interest. Requirements A
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on the individual DC projects can be found on the consortium website: www.integrate-dn.eu . Where to apply Website https://www.academictransfer.com/en/jobs/358012/16-phd-positions-to-advance-psy… Requirements
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group? Do you enjoy creating complex machines that have never existed before? Do you want to explore physics that nobody else has seen? Maybe you want to join our team as a PhD on our journey
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learn about many interesting projects related to your PhD, in particular those of the strontium BEC group and of the hybrid atom-ion group . Your PhD project will profit from synergy with all
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, neuroimaging and clinical psychiatry, with direct clinical impact. Your main activities are: analyzing and integrating multimodal MRI data for biotype identification; applying machine learning and advanced