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, or similar) will be valued; 9) Experience in machine learning techniques applied to materials science or process engineering (regression, classification, optimization, predictive models) will be valued; 10
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between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain is open to discussion. Project B – Understanding and Countering
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, therefore prior expertise in these topics are highly encouraged: Quantum Machine Learning (QML), Machine Learning on Quantum Computers, Security of Quantum Circuits, Design Automation and Tools for Quantum
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
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departments. This role offers the opportunity to advance AI applications, machine learning, and data analytics to elevate biomedical research, innovate clinical research, and develop next-generation graduate
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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pangenomics), quantum-based simulation methods for drug design, or quantum machine learning for large omics datasets. The candidate is expected to have acquired first teaching experience, and first experience
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Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Positions Postdoc Positions Country France Application Deadline 18 Jan 2026 - 17:00 (Europe/Paris) Type of Contract
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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly