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Field
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Open Positions DC 4: Use of machine learning tools for estimating EGs performance. Host Institution University Grenoble Alpes (France) Main Supervisor Alice Di Donna (alice.di-donna@univ-grenoble
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engineering, electrical engineering, data science, or a related field. Skills in embedded systems development, electronics, or IoT (C/C++, Python, Arduino/ESP32, etc.) OR in machine learning and sensor data
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synthesis over all relevant length scales (e.g. cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) • High resolution analysis, monitoring of chemistry
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at TU Delft. In this project we also work together with experimental groups at TU Delft and beyond. The Delft Bioinformatics Lab has strong algorithmic and machine learning expertise, with a profound
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. The developed new knowledge will assist performance designs, analysis, operations, and condition monitoring of sCO2 power generation systems. The project will be undertaken using the strong thermodynamic
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driving, in-car monitoring, industrial automation, and security surveillance. The research, called "R4DAR," aims to leverage emerging 4D imaging technology with Massive MIMO to create image-like radar
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data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research findings
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these sounds fascinating, then this PhD position is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts
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? No Offer Description Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15