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. We work on education and research in mechanical engineering, civil engineering and industrial design engineering. Together, we learn by making, creating, and innovating, addressing challenges in a
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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
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industrial partners. You are a naturally curious person who is eager to learn more and has a strong interest in research. Excellent written and oral communication skills in English are a prerequisite. We
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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background alongside enthusiasm and eagerness to learn is required. Additional Information Benefits A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within
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to stay or move London School of Economics, London, UK Beth Lloyd (Leiden University) Causal contributions of (zero) prediction-error signals to learning and decision-making using transcranial ultrasound
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hours. Free and unlimited access to our online learning environment GoodHabitz. Pension accrual with the ABP, of which we pay 70% of the premium. A reimbursement of your public transport expenses. Do you
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mitigation strategies. The results will support the development of evidence-based conservation protocols and can provide a learning dataset for AI informed decision that is still lacking in the domain. What we
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Qualification portfolio. You will be part of the WECARE research team, led by Dr Sonja Marzi (PI). Your supervisors will be Dr Sonja Marzi, Dr Tine Davids and Dr Edwin de Jong. Would you like to learn more about
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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming