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Field
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Your Job: The PhD project is methodologically independent, with the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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Infrastructure? No Offer Description Work group: PGI-12 - Quantum Computing Analytics Area of research: Promotion Job description: Your Job: Your PhD project will be part of an interdisciplinary research project
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two part-time positions (65%) as Doctoral Researcher (PhD Position, m/f/d) in the field of Neurobiology In accordance with § 2 of the WissZeitVG and § 72 HessHG, the positions are to be filled on a
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machine learning (ML), artificial intelligence (AI) or related fields • Software skills in ML languages such as Python • Ability and enthusiasm to learn new technologies quickly • Ability to work highly
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Researcher (R1) Positions PhD Positions Country Germany Application Deadline 30 Sep 2025 - 23:59 (Europe/Berlin) Type of Contract Temporary Job Status Part-time Offer Starting Date 1 Dec 2025 Is the job funded
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21 Aug 2025 Job Information Organisation/Company GFZ Helmholtz Centre for Geosciences Research Field Environmental science Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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two part-time positions (65%) as Doctoral Researcher (PhD Position, m/f/d) in the field of Neurobiology In accordance with § 2 of the WissZeitVG and § 72 HessHG, the positions are to be filled on a
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Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 31 Oct 2025 - 23:59 (Europe/Berlin) Type of Contract Temporary Job Status Part-time Hours Per Week 20
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 2 months ago
microbial communities from deep-sea mining test sites and polar oceans under climate change. The goal of the proposed PhD work is to design a new form of network-enabled explainable ML, as well as trained ML