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of these datasets with state-of-the-art computational methods. The successful candidate will collaborate with 2 PhD students on the project. Where to apply Website https://www.academictransfer.com/en/jobs/357581
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Can you help drive the energy transition by building a battery
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motivation to apply for this position. Curriculum Vitae with a full list of publications, and two references with their contact details. A short description (150 – 250 words) of your PhD research and a copy of
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to apply for this position. Curriculum Vitae with a full list of publications, and two references with their contact details. A short description (150 – 250 words) of your PhD research and a copy of your PhD
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description Producing carbon-neutral hydrogen at scale and low
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6 Jan 2026 Job Information Organisation/Company Wageningen University & Research Research Field Biological sciences » Biodiversity Researcher Profile Recognised Researcher (R2) Country Netherlands
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Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description You are a
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As part of the ~30 Mio € SLDBattproject , we are looking for a Postdoctoral
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Can you help drive the energy transition by simulating a battery
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. Background: You hold a PhD in Computer Science, Machine Learning, Electrical Engineering, Embedded Systems or related fields. Core Expertise: Strong expertise in Federated Learning and/or Continual Learning