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the end of the grant contract, as stipulated in Article 7 of the RBI-UC, subject to an opinion issued by the respective supervisor(s). The report models, performance evaluation criteria, and the grant
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and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative optimization and Machine Learning models to address key challenges in the future airspace
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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Responsibilities: Explore the frontiers of intelligent sensing by advancing cutting-edge theories and pioneering transformative technologies. Integrate wireless sensing with Large Language Models (LLMs) to pioneer
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(e.g., hardware trojans, side-channel exposure). Co-develop testbenches for hardware simulations and chiplet-level threat modelling. Collaborate closely with FPGA and IC prototyping teams to deploy AI
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quasi-experimental methods to identify causal effects and test the predictions of economic and sociological models. Examples of current research projects include: long-term impacts of neighborhoods and
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frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well-documented code. Knowledge
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(animal models) studies in preparation of potential first-human studies. For this purpose, there will be a tight interaction between the Cancer B-cell group and Prof. Mark Cragg at the Antibody Vaccine
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snow in local and regional climate models is poorly constrained, leading to uncertainties in estimating mass loss through sublimation and snow redistribution. The PhD candidate will develop and execute
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our team focused on advancing kidney gene therapy. The project integrates in vitro, ex vivo, and in vivo experimental approaches using both porcine and human models. Appointment Details · Start