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Field
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applicability. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse
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expertise in biodegradable drug-delivery technologies (https://www.mgshell.com ) and Politecnico di Milano’s strengths in microfabrication and digital machining. Secondments include MgShell (3 months
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the structural and electronic properties of complex materials. Using first-principles simulations, machine learning techniques, and advanced Monte Carlo methods, the student will develop predictive
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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? To address these questions, the PhD candidate will develop and evaluate new decision-support methods that combine optimization and machine learning. Particular attention will be given to methods that can
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master in Computer Science, Bioinformatics or related field Strong programming skills in R Background in machine learning and/or protein structures Fluent in English Specific Requirements Mobility rule
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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adaptive resource allocation, AI-driven network orchestration, dynamic beam steering, joint communication and sensing, and cross-layer optimization. Machine learning techniques will be investigated
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together to develop solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/phd-position-in-modeling-and-c… Requirements Research
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FieldComputer scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Strong foundations in Machine Learning and Deep Learning Excellent Python programming skills Experience with PyTorch