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Supervisor: Dr. Kamila Maria Jozwik, Jozwik lab PhD fees status: Home fees only (https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status ), 4 years Start date: October 2026 The
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for the division is the Visualization Center C, a unique science center in Norrköping that engages the public through data-driven installations and 3D fulldome experiences. Read more at http
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: Shape the future of immersive visual technologies through optical, computational, and machine‑learning innovation. We are a university research group at imec-VUB in Brussels, with expertise in optical
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strategies to reduce hallucinations or improve model confidence Evaluate and benchmark against state-of-the-art methods the reliability and robustness of the hallucination detection system under diverse visual
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of existing methods and procedures for use in the joint project Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with other project partners and regular
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of existing methods and procedures for use in the joint project Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with other project partners and regular
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Responsibilities The Visualization Design Lab at Graz University of Technology is seeking a highly motivated Predoctoral Researcher (PhD Student) at TU Graz. The student will be supervised by Prof. Alexander Lex
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DTU Tenure Track Researcher on Nanoreactors for Operando Visualizations of Nanoparticle Catalysis...
can read more about Center for Visualizing Catalytic Processes (VISION) at https://vision.dtu.dk . If you are applying from abroad, you may find useful information on working in Denmark and at DTU
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to fine-grained visual understanding and anomaly detection under limited, weak, or imperfect supervision. The precise research direction will be defined together with the candidate and may evolve over
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 22 days ago
of the trajectory. This grant targets the development and rigorous benchmarking of novel, energy-efficient CAD-based deep learning architectures that fuse visual and inertial modalities for the robust 6D pose