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Field
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science, or public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced
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skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX or PyTorch, is a plus. In addition to above-average interest in the topic
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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properties through tuning of concentrations and types of viscosity modifiers and superplasticizers, deep learning modeling of parameters of cement composites. The project is realized at the Bydgoszcz
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or equivalent) Strong programming proficiency in Python Experience with deep learning frameworks such as PyTorch and/or TensorFlow Experience using data science libraries (e.g. NumPy, Pandas, SciPy, scikit-learn
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information sources and to provide the relevant analysis of all the available variables in different scenarios conditions. In order to reach this goal, deep learning-based algorithms will be implemented
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. Contribute to Europe’s photonics ecosystem through involvement with PITC, JePPIX, and Chips for Europe initiatives Where to apply Website https://www.academictransfer.com/en/jobs/358372/phd-on-compact-models
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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling
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reports to develop computational models that predict identification reliability. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and
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Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models