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robust models – and for clinicians, whose goal is to determine when to trust the models. We therefore seek candidates who have strong technical background in working with large-scale deep learning models
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optimization, for enhancing light trapping in nanostructured thin-film solar cells. Your role will focus on developing and applying large-scale electromagnetic simulations to identify optimal nanostructured
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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opportunity to be part of large-scale experiments tackling pressing societal challenges. You'll be involved in every stage of the research process—from experimental design to data analysis and publication—while
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] that process information in temporal rather than spatial modes to reduce their footprint. The project involves a collaboration between DTU Electro (Senior Researcher Mikkel Heuck) and Harvard University (Dr
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and North, ensuring real-world testing across diverse agricultural contexts. Beyond the research itself, IBIS aims to implement standardized experimental protocols, harmonized data formats, and
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qualification, you must hold a PhD degree in computer science, software engineering, biomedical engineering, data science, or a similar field. Your project management skills include: Experience in technical
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observations from the Kepler and TESS satellites, complemented by Gaia data and ground-based spectroscopic and spectropolarimetric observations. See more information here: https://www.space.dtu.dk/english
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-based solutions to societal challenges. We have a long-term involvement in applied and interdisciplinary research, big data and data science, artificial intelligence (AI), the Internet of Things (IoT
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to enhance imaging quality and speed Creating efficient data acquisition and processing workflows for large datasets of skin nanotexture images Optimizing hardware-software integration for robust performance