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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for one or more postdoc positions in the field of advanced antenna architectures for small
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non-von-Neumann computing architectures. The project explores hybrid CMOS–spintronic computing systems, leveraging emerging spin-based devices such as magnetic tunnel junctions (MTJs) to enable ultra
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design platforms for rapid deisgn and optimisation of novel targeting modules. Your responsibilities will include: Designing and implementing state-of-the-art deep learning architectures for protein
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qualifications You will lead the computational and AI-driven aspects of the project. Your responsibilities will include: Designing and implementing state-of-the-art deep learning architectures for protein
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cycle, from architecture to implementation, of a CMOS-based digital neuromorphic processor. Contribute to the design of a test setup for prototype validation in collaboration with the PhD student who is
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Job description - Developing device-to-architecture level models of emerging nanoscale devices (spintronic, resistive, or hybrid) for in-memory and neuromorphic computing. - Exploring hardware-level
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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and adaptive token pruning; Distributed and collaborative inference strategies; Mixture-of-Experts (MoE) architectures for scalable inference; Resource-aware and latency-constrained inference
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collaboration with project partners. Participate in the full design cycle, from architecture to implementation, of a neuromorphic image sensor. Contribute to the design of a space-grade test setup for prototype
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architectural design, including but not limited to ReACT/CodeAct agents, multi-agent systems, self-evolving agents, scalable agentic memory management and Extensive knowledge of existing bioinformatic algorithms