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evolutionary conservation across vertebrates in terms of muscle pattern and innervation modes (1–3). The coexistence of both vulnerable and resilient muscles within the same organism provides compelling in vivo
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on the design of more efficient catalysts. Scientific Approach To meet these objectives, the PhD candidate will employ an integrated approach combining experimental and computational techniques. First
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CNRS (Centre national de la Recherche Scientifique) | Marseille, Provence Alpes Cote d Azur | France | 2 months ago
Researcher (R1) Positions PhD Positions Country France Application Deadline 15 Apr 2025 - 12:00 (Europe/Brussels) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 2025 Is the job
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. The project is led by Heiko Schütt and will employ one PostDoc and one PhD student. About the role... You will develop new Bayesian methods to compare deep neural network and other artificial representations
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referee A brief (around 500 W) sketch for a potential PhD project (research question, a short literature review, an overview over the methods and data that you aim to use), related to the design and topics
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knowledge sharing and the acquisition of transferable skills with a focus on entrepreneurship and innovation. Through the multi-dimensional training of the FunctiGlass programme, the 11 PhD candidates will
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Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 4 Jul 2025 - 23:59 (Europe/Paris) Type of Contract Temporary Job Status Full-time Hours Per Week 40
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» Metrology Physics » Optics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 31 Jul 2025 - 17:00 (Europe/Paris) Type of Contract Temporary Job Status
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Research Fund (FNR) in the framework of its Program for Research Intensive Doctoral Education (PRIDE ), we are currently offering 5 PhD (Doctoral Candidate) positions for a duration of up to three years
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The SiMul team (https://cran-simul.github.io) at the University of Lorraine is offering a fully funded PhD position on the theoretical foundations of self-supervised learning, focusing