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of electron microscopy imaging and spectroscopy to reveal the structure–property relationships that govern molecular adsorption mechanisms. This interdisciplinary project is fully funded by DTU’s PhD grant
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: Specialization in machine learning and computer vision. Strong skills in developing computer vision and AI models. Experience with thermography or thermal imaging. Experience in data fusion and multimodal learning
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energy-efficient CMOS blocks implementing SSM-based LLMs. Prototype hardware blocks on FPGA and prepare for ASIC tape-out. Benchmark performance and comparison with transformer accelerators. Work with
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thermochemical TES. Your main supervisor will be Prof Adriano Sciacovelli and you will join the Thermal Energy Section at DTU Construct. Your work will contribute to a paradigm shift in how complex TES systems
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for the globally expanding healthcare sector with its demands for the most advanced technological solutions. DTU Health Tech’s expertise can be described through five overall research areas: Diagnostic Imaging
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-XFEL and ESS for advanced materials studies and is host for the 3D imaging center Plasma Physics and Fusion Energy working in international collaborations on realizing non-fossil-based energy
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to 10 years by the Danish National Research Foundation (https://dg.dk/en/) and represents a collaboration between the Faculty of Humanities at the University of Copenhagen and the National Museum
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predictors in an efficient closed loop paradigm, to optimize the fitness as well as the information gain in each cycle. Responsibilities Propose AI models for an integrated approach for binder design, using
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CMOS analog/mixed-signal circuits (Cadence, Spectre, etc.) Optimize circuits for power, noise, and area constraints Prototype and validate circuits with EEG signals and wearable sensors Collaborate with
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we collaborate with the best universities around the world. Job Info Job Identification 5674 Job Category Phd Posting Date 08/11/2025, 07:02 AM Apply Before 09/14/2025, 10:59 PM Locations Henrik Dams