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Field
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2 Oct 2025 Job Information Organisation/Company Tallinn University of Technology Research Field Computer science » Programming Computer science » Other Engineering » Control engineering Engineering
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supervised deep learning algorithms for 3D laser data from forests Developing self-supervised deep learning algorithms for 3D laser data from forests Expand for a wider variety of downstream tasks focused
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; validating models using existing offshore datasets, lab-scale experiments, and collaborating with ongoing projects; developing optimisation algorithms for ensuring techno-economic feasibility of FPV systems
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Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Reference Number 101169370-DC4 Marie Curie Grant Agreement Number 101169370 Is the Job related to
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matter physics, and quantum computing. You will dive deep into the realms of theoretical and computational physics with a focus on the development of algorithms and large-scale numerical simulations. Your
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university, the internationally accredited registrar and classification society DNV, and Cancer Registry of Norway. You will be analyzing and developing algorithms for privacy preserving health registry data
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of additively manufactured Mg alloys for biodegradable implant applications. Host: Helmholtz-Zentrum Hereon
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Application Deadline 3 Oct 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Nov 2025 Is the job funded through the EU Research Framework Programme? Not
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, machine learning or similar. Alternatively, you have gained essentially corresponding knowledge in another way. The applicant is expected to have good knowledge of computer science, mathematics, algorithms
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combining high-fidelity computational modelling with artificial intelligence to overcome key barriers in performance. The investigation will focus on optimising core gas exchange and combustion processes