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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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. Lähdesmäki. “Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport”. In: Proceedings of the 43rd International Conference on Machine Learning. OpenReview . 2026. What we offer
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loading, and functional genomics. 3. Computational Modeling of pHLA Recognition and Protein Design Computational protein design, machine learning, molecular recognition, and predictive approaches
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técnicas de machine learning e IA. Sólidos conocimientos en análisis y tratamiento de datos, programación y desarrollo de modelos analíticos. Experiencia con bases de datos y herramientas de análisis
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deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine which features are relevant? These questions
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. To analyze and control thermoacoustic instabilities, we combine fluid mechanics, acoustics, and combustion with methods from control engineering, nonlinear dynamics, and data-driven modeling (machine learning
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prostate, bladder and kidney cancer). The program will draw on expertise in probabilistic modeling and causal machine learning, phylogenetic and evolutionary inference, clonal dynamics, and immune-tumour
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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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, microplastics; by means of numerical and experimental approaches including high-performance computing, data science methods via machine learning/AI and digital twins, enhancement, development and of application
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flexible learning models. Strong understanding of education pathways, stackable credentials, and learner progression. Ability to collaborate on learner recruitment and outreach efforts in partnership with