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the job funded through the EU Research Framework Programme? Horizon Europe Is the Job related to staff position within a Research Infrastructure? No Offer Description Job Reference: Ref.68.25.407
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think outside the box, to learn fast, collaborate effectively, iterate quickly, and work at the interface of both experimental and computational design. Qualifications for Computer Scientists, AI/ML: PhD
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cloud computing; Latex Does this position have supervisory responsibilities? No Preferred Education/Experience PhD in computer science, physics, engineering, statistics or applied mathematics is required
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
Researcher Profile Recognised Researcher (R2) Positions PhD Positions Country Portugal Application Deadline 5 Oct 2025 - 23:59 (Europe/Lisbon) Type of Contract Not Applicable Job Status Not Applicable Offer
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PhD or MSc in a relevant field of research (Bioinformatics or Computational Biology). Extensive experience in R or Python software development. Understanding of types and properties of mass spectrometry
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, such as sedimentation, meltwater flow, and vegetation change, into active drivers of adaptive design. This interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and
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reviewed scientific publications. Your profile PhD in Computer Science, with specialization on applied machine learning, statistical methods, and/or software engineering Strong programming skills Strong
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& Experience (specific to the project): PhD in Computer Science, Cybersecurity, Artificial Intelligence, or a related discipline. Strong research background in virtualisation, cybersecurity, AI or threat
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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classification, segmentation and regression using combinations of high-resolution LiDAR point cloud data and image data and may draw upon aspects of synthetic data-based learning and domain adaptation. To learn