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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
. The successful candidate will be expected to carry out the following scientific activities: Application of advanced Machine Learning, Deep Learning, reduced-order modelling, and physics-informed/-guided modelling
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be advantageous: Experience with scientific expeditions Experience in computer-aided analysis of biological sequence datasets (e.g., with R, Python and Bash/Linux environments) Basic understanding
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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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attention to quality, integrity, creativity and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
- SIPG LAB/ISR and 1801P.01460.1.04 LARSYS/ISR BASE 2025-2029 – SIPG LAB/ISR, financed by national funds through FCT/MCTES Workplan: Develop new representations for computer vision and machine learning
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and research interests, the project may focus on one or more topics such as cyber risk assessment, human–machine interaction, cyber risk communication, cybersecurity training, or organizational and
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modeling, geometric deep learning or physics-informed machine learning, or you are willing to learn these quickly; strong collaboration skills: you enjoy working in a multidisciplinary team and feel
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of electrolyzer technologies, digital twins, model order reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners