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in advanced methods for the analysis and classification of EEG and auditory signals. The group of the project is multidisciplinary, with experts in signal processing, machine learning, acoustics and
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Offer Description Funding: 36 months, CIFRE (https://www.anrt.asso.fr/fr/le-dispositif-cifre-7844 ) Starting date: November / December 2025 Keywords: Physically informed machine learning, Industrial
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: electronic structure calculations (plane wave DFT if possible), statistical thermodynamics, molecular dynamics. Skills in Python, bash scripting, Fortran 90 and machine-learning would be appreciated. The PIIM
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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 12 days ago
enabler of machine learning for eDNA-based assessments of deep-sea ecosystems” (m/f/d) Background Deep-sea ecosystems host highly diverse biological communities that provide key ecosystem functions and
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. The candidate will also collaborate with the Department of Computer Science at Kiel University and the remote sensing company EOMAP GmbH, employing state-of-the-art machine learning techniques to improve
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Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15 doctoral projects
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and small, contribute to a better world. We look forward to receiving your application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with
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Join us to explore the mechanics of soft matter through a unique blend of theory, hands-on experiments, and machine learning. Job description Soft matter such as polymers and hydrogels
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters