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19th June 2026 Languages English English English We are looking for a PhD candidate at the Department of Engineering Cybernetics PhD Candidate in Data-driven optimization for Energy Communities
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, and antioxidant properties), the aim is to link biomass and process with composition and functionality. To accomplish this, the project will explore different approaches for data and correlation
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engineering Engineering » Systems engineering Engineering » Process engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 19 Jun 2026 - 23:59 (Europe/Oslo
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between 2026 and 2029 and includes experimental campaigns, field installations and data collection, data processing, algorithm development and system optimization. Job description Experimental Campaigns and
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process Main ideas of the proposed dissertation topic: The topic of the dissertation is focused on the creation and use of virtual and augmented reality in the production process. The doctoral student
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, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims
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(photomixing). The candidate will work primarily in the IEMN cleanroom, developing and optimizing micro/nanofabrication process flows for laser device realization. A first research axis will focus
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
. These simulations are run on highly parallel supercomputers on which both the hardware and the software are optimized for the task at hand. While the computing power of each processing unit is still increasing, the
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fellows and students, maintaining laboratory equipment, contributing to in vivo experiments in small animals (mice), and leading optimization studies using existing biological data within the CTRC. Propose
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Generative AI applications continue to expand, optimizing computational efficiency is becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge. To address