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Science, Computer Science, Data Science, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is
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skills with experience in cloud data warehouse systems such as Snowflake and data preparation tools like Tableau Prep Experience with predictive modeling, machine learning, AI applications, and advanced
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execution and milestone completion. Job Requirements Strong background in AI/NLP or speech technologies, with experience in designing and implementing machine learning models. Proficient in software
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these short-lived events are notoriously hard to reconstruct and to model, so our understanding of their behaviour during warmer climates is limited. To learn from past warmer climates and better understand
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and machine learning based modelling. Publish research data, prepare project progress reports. Assist in the preparation of grant proposals. Job Requirements: At least a Bachelor's degree from a
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composites for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 6 days ago
clinicians and healthcare partners. Main thematic axes AI-based predictive modeling in healthcare Risk prediction, outcome forecasting, and early warning systems Time-series and multimodal deep learning
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize