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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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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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/admittance, force control Experience with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g
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/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game theory and theory of metric spaces (with
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication
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machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
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Squamous Cell Carcinoma Therapeutic Targets Using Systems-Level Machine Learning Based On Single Cell RNA Sequencing", implemented under Financing Agreement No. 760066/23.05.2023 The “Iuliu Haţieganu
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THE PROJECT PNRR" Lung Squamous Cell Carcinoma Therapeutic Targets Using Systems-Level Machine Learning Based On Single Cell RNA Sequencing", implemented under Financing Agreement No. 760066/23.05.2023
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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical
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and resistance. Through close collaboration between laboratory and clinical teams, our work bridges mechanistic immunology with real-world patient outcomes. To learn more about Hosoya Lab - https