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Field
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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This PhD project aims to achieve a breakthrough in thermal efficiency (>50%) for dual-piston Free-Piston Engine Generators (FPEGs). The research will pioneer a novel, integrated methodology
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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intelligence, NLP, machine learning, or a related field Experience with Python and Generative AI libraries (e.g., Huggingface Transformers) Knowledge of Multimodal Generative AI models and their corresponding
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30 Oct 2025 Job Information Organisation/Company Warsaw University of Technology - Centre for Credible AI Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD
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in Computer Science, Artificial Intelligence, or related field. Solid programming and development skills (Python, Git, Bash). Experience with machine learning (e.g PyTorch/TensorFlow). Strong interest
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on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
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awardee. The Opportunity Generative artificial intelligence is a significant and highly visible use of machine learning which has become commonplace in a matter of a few short years. Without common
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data collection strategy using the Internet of Things, sensors and NMEA 2000, a data analyzing plan using established guidelines and machine learning tools. We offer: A fully funded PhD position for four
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tools Computer science » Systems design Engineering » Chemical engineering Engineering » Computer engineering Engineering » Design engineering Engineering » Industrial engineering Engineering » Materials