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optimization of these building blocks are essential for achieving breakthroughs in data rate, power efficiency, scalability, and overall system cost. This PhD project focuses on the electronic integrated circuit
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Job description We invite applications for a fully funded PhD position in the area of Scientific Machine Learning (SciML), which integrates data-driven machine learning techniques with established
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4th October 2026 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Digital Sovereignty Apply for this job See advertisement This is NTNU NTNU
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The smart optical networks lab (SONL) at the Electro-Optics Communication (ECO) group at Eindhoven University of Technology is recruiting for a PhD position to research novel fast controlled photonic
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2 PhD Positions in Computational Modelling of Biological Membranes and Adaptive Functional Materials
simulations, membrane biophysics, physical chemistry, soft condensed matter or computational structural biology. PhD Project 2 – Adaptive Functional Materials for Neuromorphic Computing (CRC 1459) Adaptive
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» Nuclear engineering Engineering » Control engineering Physics » Neutron physics Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application
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» Mechanical engineering Engineering » Industrial engineering Engineering » Chemical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 14 Oct 2026 - 23:59
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First Stage Researcher (R1) Positions PhD Positions Application Deadline 8 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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First Stage Researcher (R1) Positions PhD Positions Application Deadline 8 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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design spaces, long-duration simulations, and real-time applications. This PhD project will develop physics-informed deep learning and surrogate modelling approaches to accelerate simulation, uncertainty