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standard track (30 months at IMT Atlantique + 3 months at University of Waterloo, Canada where the PhD student will stay 3 months at Prof. Ricardez’ lab. + 3 months at a non-academic partner). 1.1 Domain and
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
with setting up a streamflow forecasting system in Portugal and the advancement of scientific knowledge in machine learning probabilistic hydrological forecasting and decision-making optimized to act on
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, neuroscience, machine learning, or related fields and/or merit/distinction-level performance in a relevant postgraduate degree (e.g. MSc) Experience of working in a neuroscience, clinical or engineering research
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of dissertation topics: Development of Machine Learning Frameworks for Reactive Atomistic Materials Modeling (DSP II) Profile of the graduate This Ph.D. program is an interdisciplinary study combining physical
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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equivalent fields of study. Position 1: In-depth knowledge in the areas of Biomedical Visualization, Biomechanics, Machine Learning, Development of Server/Client Applikationen, Daten Management. Position 2: In
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-aligned Intelligence and Novel Exploration) group (Prof. I. Bogunovic) at the Department of Mathematics and Computer Science, University of Basel, is inviting applications for multiple PhD positions in
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Vacancies PhD position Social dynamics of energy communities in the Dutch energy transition Key takeaways Social dynamics shape behaviours that can accelerate or hinder the Dutch energy transition
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. The successful candidate will be based in Odense, under the primary supervision of Prof. Ricardo J. G. B. Campello , but they will be expected to also work closely with other PhD students, postdocs, and
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts