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and build a light-sheet encoded-illumination microscope Characterize the system Sample Chamber Development Development of Decoding Algorithms Single-Plane SR Imaging Volumetric Imaging of selected
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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Brushless Doubly Fed Machines (BDFMs) are emerging as a promising technology for offshore wind turbine generators due to their fractional-sized power converters, elimination of rotor brushes and
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices
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oceans. The PhD student will be supervised by Dr Drage, and will closely collaborate with several international colleagues, primarily Prof. Minter (University of Portsmouth, UK) and Dr Pates (UCL, UK). The
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the experimental setup, synchronising the cameras, optimising the illumination, and developing image-processing methods to extract propagation pathways and velocities. Several plant species and different branching
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, societal, and technological aspects of the environment shape this process. Working across disciplines such as psychology, neuroscience, computer science, economics, and sociology, we do research within a
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modern machine learning, statistical signal processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs
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1 Sep 2026 Job Information Organisation/Company Instituto Politécnico do Porto Department CIICESI Research Field Engineering » Computer engineering Researcher Profile Other Profession Positions PhD
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join a unique project which aims to develop the next generation of green and low-carbon hydrogen technologies. You will be embeded in the Process & Energy department of the Mechanical Engineering Faculty