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of metabolic network modelling linked to epigenetics Carry out machine learning, and integrative analysis of large epigenome datasets Communicate research results in international conferences and journals Work
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: www.uni.lu/snt-en/research-groups/finatrax/ The candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with specialisation in Information Systems. In the context of Prof
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framework to bridge this gap and enable organizations to confidently deploy secure GenAI solutions by evaluating the machine-learning models intrinsically, identifying components of an AI pipeline and their
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-connectivity Communication system modelling, performance analysis, and simulation. Optimization tools and machine learning techniques. Hands-on experience with software-defined radios (SDRs) and/or
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activities across these decentralised and increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project
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to optimization problems with possible topics covering: Variational quantum algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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training unit: https://www.list.lu/en/research/project/forfus Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? Your PhD work will focus on outdoor forest