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Field
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: Your consent. Data retention period: One year. If you are selected, as long as the employment relationship is in force and legal responsibilities may arise. Data sharing: Does not occur, except
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Understanding factors related to student retention and experience in physics and astrophysics major units. Using quantitative (surveys) and qualitative data (interviews with students) this project
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, packaging, `pytest`, profiling, `asyncio`, memory-aware data structures. You write unbuffered, logged, restartable long-running jobs by default. Training loops written from scratch, custom datasets and
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Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing power, latency, and memory bandwidth issues with reliable
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learning techniques, and dynamic prompting with conversational memory mechanisms. Funded by the European Funds for a Modern Economy (FENG) 2021–2027 Priority 2, under the First Team competition. Reference
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learning techniques, and dynamic prompting with conversational memory mechanisms. Funded by the European Funds for a Modern Economy (FENG) 2021–2027 Priority 2, under the First Team competition. Reference
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learning techniques, and dynamic prompting with conversational memory mechanisms. Funded by the European Funds for a Modern Economy (FENG) 2021–2027 Priority 2, under the First Team competition. Reference
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the lifetime of the device. Consequently, energy consumption, memory usage, and computational requirements remain fixed, even when the application or environmental conditions evolve over time. This project
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implementation can introduce vulnerabilities that are not visible at the algorithmic level. Sensitive information may leak through power consumption, electromagnetic emissions, timing behaviour, memory or bus
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sensing and AI processing. The focus lies in architecting a unified system capable of real-time image intelligence, overcoming the critical hardware bottlenecks of limited memory and power to enable