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learning, as well as process-based models integrating thermal biology mechanisms. Funded by the ”Federal Ministry of Research, Technology and Space” (BMFTR), CSIDlab as part of the “Vector-borne disease
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levels, - policy design and impact analysis, - resource allocation, growth and welfare evaluation, - technology diffusion and firm level innovation. leading to an internationally competitive PhD degree and
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to obtain further academic qualification (usually PhD). Tasks: scientific research and development activities in research data management (RDM) with a focus on AI- and machine learning-based methods
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Karlsruhe Institute of Technology (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 2 months ago
test non-invertibility using machine learning attacks Protocol design. You conceptualize a proof of physical work protocol and design attack strategies against it Blockchain simulation. You simulate a
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Description Main supervisor: Ants Kallaste Co-supervisor: Anton Rassõlkin The research Within this thesis, the PhD candidate will learn about the control and application of additively manufactured special types
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within a Research Infrastructure? No Offer Description We are looking for a highly motivated and ambitious PhD candidate with experience in either biomedical engineering, machine learning, polymer
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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28 Oct 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Would you like to work at the intersection of transportation, robotics and machine learning
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap