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Field
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projects are within the overall theme of neuromorphic computing and analog signal processing, targeting applications in the fields of communication, sensing, geolocalization, space and biomedical. This PhD
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scenarios S1 and S3 representative of the interactions between the acidic solution stored in the reservoir rock and the sealing caprock. To this purpose analog materials will be designed which will allow
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following possible projects: Bionic Vision microelectronics: In this project, you would develop advanced low-power mixed-signal (analog + digital) circuitry on CMOS chips which will drive our custom implants
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and interpretability, analogous to RAG (Retrieval-Augmented Generation) in LLMs Investigating methods for improving AI model sustainability, e.g. model compression techniques (such as quantization and
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cues play a role analogous to conditioned stimuli: they are signals that, once learned, allow the agent to anticipate the consequences of its actions. Scientific Motivation: Learning-based navigation
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, electromagnetic waves and antenna design. Knowledge about analog and digital RF signal processing. PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application
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antenna design. Knowledge about analog and digital RF signal processing. PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. The appointment
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. Required Qualification Solid background in electronic hardware design, including experience with PCB design and off-the-shelf components. Solid background in analog/mixed-signal ultra-low power CMOS circuit
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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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analog circuits for implementing ONNs for computing. Modeling, simulate and benchmark different computing tasks such as sensor data processing. Explore ONN implementation topology and its energy efficiency