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-oriented background - You have a genuine interest in signal processing and machine learning methodology and algorithms - You obtained good grades in courses related to the topics relevant to this PhD
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modelling, learning-based reconstruction and classification algorithms, enabling joint optimisation of optics and data processing. We are looking for an excellent PhD candidate in photonics (48-month duration
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Description Function We are looking for one highly motivated PhD candidate with a master's thesis in cell biology, molecular biology, neurosciences or genetics to join our research group in the Development
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reinforcement learning algorithms and contribute to the joint development of the broader modelling and policy framework. Your work will focus on multi-criteria reinforcement learning, uncertainty-aware decision
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algorithmic, hardware, and platform engineering teams. What you will do You actively shape imec’s research and engineering roadmap by tackling the following core architectural challenges: Architecting endtoend
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embedded AI-based algorithms. The applicant should: have a Master’s degree in Electrical Engineering, be ranked within the top 10% of their class in MSc and BSc, and have exceptional grades, have a strong
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‑world adoption. Role Overview We are seeking a postdoctoral researcher to define and execute an ambitious research agenda at the intersection of generative AI and algorithm-hardware co‑design for next
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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid
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algorithms that can tightly integrate physical hardware, sensors, and computational models. This PhD position is centered on addressing these challenges through innovative computational methods, combining
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learning-powered algorithms as well as hybrid approaches, combining either reinforcement learning or deep learning (Graph Neural Networks) with human-based modelling, for fully flawless and autonomous method