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Field
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of inverse photonic design, machine-learning (adjoint or topology optimization), or other advancing design methodologies. Ability to design and model active photonic devices (MEMS, electro-optic or thermo
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implement a hyperspectral imaging system tailored to bulk forensic trace analysis and develop chemometric and machine-learning models for material identification and classification. You will evaluate
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catalysts used in chemical processes. The successful candidate will combine state-of-the-art quantum chemical modelling alongside machine learning techniques and contribute to the development of predictive
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looking for a full-time (100%) doctoral scholarship holder in the field of machine learning for circular polyurethane design. Position You will actively work on the preparation and defence of a PhD thesis
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. To promote a strategic career path, all PhD fellows are expected to submit a career development plan, specifying career goals and the competencies that the PhD fellow should acquire, no later than one
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Are you interested in exploring how multi-agent aerial manipulation can contribute to construction and working at the intersection of robotics and machine learning? Job description Advancements in
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://www.academictransfer.com/en/jobs/362880/phd-position-on-critical-approa… Requirements Specific Requirements You have a Master's degree in Industrial Design, Interaction Design, Human-Computer Interaction, or a closely
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health. Successful candidates may have experience in electron or X-ray microscopy, image analysis, AI and machine learning, quantitative data science or computational modelling. They will be able to work
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electrical engineering, control engineering, applied mathematics, computer science, or a related field A strong background in probability and statistics, machine learning, or control theory Interest in cyber
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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
to significantly accelerate computations while maintaining sufficient physical accuracy for geoscience applications. Inspired by methods from computer graphics, this approach could enable unprecedented resolutions