40 programming-"Multiple"-"U"-"Prof"-"O.P"-"Edge-Hill-University"-"U.S" positions at Nature Careers in France
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experimental data and is testable across multiple unlearning scenarios. For this we plan to apply for the first time Spiking Neural Networks (SNNs) to the modeling of unlearning. SNNs have recently shown
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. The RELIANCE population-based research pilot study was initiated under the umbrella of the “Plan National Cancer 2020-2024” (PNC2) and is funded by the Directorate of Health (DiSa) of the Ministry of Health
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file named in the following format: LAST NAME of the candidate_Last Name of the supervisor_2023.pdf Description of the topic: Federated learning (FL) enables multiple stakeholders to collaboratively
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We are recruiting a highly motivated and experienced researcher to develop and structure a research and educational program dedicated to kidney cancer. The position is supported by a 3-5 year fixed
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. Indeed, when multiple sources exist in the vicinity of a same sensing unit, their signatures mix and estimation of individual sources is disturbed by the other co-occurring sources. The aim of the doctoral
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: Contribute to curriculum development and educational programs Develop and deliver lectures, seminars, and workshops related to ATM radiology and medical imaging technologies Supervise and mentor graduate
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-source tools and has a long history in coupling natural language techniques and knowledge graphs. The R&D programme for that position includes several tasks: Generalize and abstract the bot from specific
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is replaced by multiple objectives or by satisfactory balance between different criteria. References [1] J. A. Bærentzen, J. Gravesen, F. Anton, and H. Aanæs, Guide to Computational Geometry Processing
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-intensive PhD training programme, supported by the PRIDE funding scheme of the Luxembourg National Research Fund (FNR) and the programme's partner institutions: University of Luxembourg, Luxembourg Institute
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, or examples, these aspects are of utmost importance and need to be explored to provide convincing and well-grounded arguments [1]. This PhD program will propose to explore advanced methods to detect implicit