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opportunities for scientific leadership and industry engagement. Eligibility criteria Applicants hold a PhD in Computer Science, Software Engineering, Cybersecurity or a related field, have a strong background in
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operation. To design optical metasurfaces and material platforms exhibiting time-varying responses. Using adjoint-based optimization and spatial structuring, to realize complex time-modulated medium dynamics
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of performance targets such as efficiency, bandwidth, and multi-condition operation. To design and fabricate reconfigurable metasurface prototypes that implement a global tuning mechanism, and to experimentally
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possibility of extension) Start date: August 2026 at latest The students will be enrolled in the structured PhD programme in Computer Science at Sapienza University of Rome, Italy: https://www.uniroma1.it/en
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cytometers Train and support users for independent equipment and software usage Ensure smooth operation of the facility, including related administrative and organizational responsibilities (e.g. financial
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, graphs); experience with analysis and processing of large volumes of data; development of reproducible scientific software; proficiency in Python and libraries (Pandas/NumPy and PyTorch/TensorFlow/Scikit
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science » Computer systems Computer science » Modelling tools Computer science » Informatics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 May 2026 - 23:00 (Europe/Paris
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sciences with software programming skills in SAS, JMP, R Statistical Software, or others and a strong publication record. A post-graduate degree (PhD, DVM or equivalent) in veterinary, animal, or
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(the Million Tutoring Moves [MTM] dataset), along with open-source applications that support data processing and analysis grounded in Responsible AI principles. A core part of the NTO’s mission is to develop
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reliably stratify patients based on their fracture risk. The position is funded as part of Horizon 2020 European Project (https://www.metastraproject.eu/ ) that aims to provide a combination of models