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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
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strong understanding of computer hardware or VLSI design. The selected candidates will contribute to the development of: A Physical-to-Electrical Abstraction and Modeling Engine A Circuit-Level Abstraction
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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computer models as well as being in the lab. You have affinity with analytical determination of pharmaceuticals in difficult matrices. You are able to dive into topics outside your current knowledge base
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, PlasmaObs, LCRS, Moonlight and Henon. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s) of activity/research for the traineeship Many challenges and trends will affect the operations
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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
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. The current position entails conducting experimental research, publishing in peer-reviewed journals, presenting at conferences, and mentoring BSc and MSc students. The group values open interaction, knowledge
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in electrical engineering, computer Engineering, or a related field. Strong background in analog and mixed-signal circuit design and simulation. Familiarity with IC design tools and tapeout flow and
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master’s degree in electrical engineering, computer Engineering, or a related field. Strong background in mixed-signal circuit design and simulation. Familiarity with IC design tools and tapeout flow and
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models that provide evidence-based reasoning for mission-critical decisions. Explainable AI for mission-critical decision support: design interpretable machine learning architectures capable of offering