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, contact or collision sensors, proprioceptive arrays) on mobile platforms (wheeled/rolling and/or aerial). Designing and integrating a soft shell for protecting the robot from collisions. Developing methods
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Adultos of the Recovery and Resilience Plan of Portugal. 3. Admission Requirements Applicants must be: Students enrolled in a PhD programme, or Students enrolled in non-degree courses integrated in
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
Website https://jobs.inria.fr/public/classic/en/offres/2025-09541 Requirements Skills/Qualifications Eligible candidates should have a PhD in computer science. Experience in formal methods or in
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languages: Danish and English. You are expected to be fluent in at least one of these languages, and in time are expected to master both. As formal qualification you must hold a PhD degree (or equivalent
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Researcher (R1) Positions PhD Positions Country Portugal Application Deadline 18 Nov 2025 - 17:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework
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grants awarded directly by FCT, I.P. https://www.fct.pt/fct-atualizou-o-valor-das-bolsas- de-investigacao-3/, and payment is made monthly by bank transfer. Selection methods: The evaluation will be based
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the following conditions: 1. Scientific Area: Medical Biotechnology (sub-area: Biotechnology for Health). 2. Recipients profile/category: PhD in Engineering and Management or related areas. Mandatory requirements
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, equivalence or registration of the doctoral degree, under the terms of the applicable legislation. This formality must be completed by the signing date of the contract. 4.1. General admission requirements
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Clinical and Translational Research. They will also be expected to provide formal didactic teaching to graduate and clinical trainees enrolled in the Master’s and PhD programs of the Department of Medicine
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: Verifiable world models. The research will focus on developing a new class of structured, verifiable world models that integrate the flexibility of deep learning with the rigor of formal methods and