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Field
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research. Experience applying machine learning and statistical modeling techniques to large biological datasets for biomarker discovery, disease prediction, or host-pathogen investigations. Proficiency in
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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Computer Science, Electrical/Electronics Engineering, Data Science, Cyber-Physical Systems, or a closely related discipline. Machine Learning & Data Processing: You have solid experience developing and applying
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Master’s degree in physics, chemistry, materials science, chemical engineering, or a related field who are excited about applying machine learning and data science to real-world materials challenges
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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of pre-eclampsia Research area and project description: Pre-eclampsia affects 1 in 10 pregnancies, yet diagnosis remains uncertain. This PhD will integrate clinical data and blood biomarkers to improve
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon
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collaborative, data driven, computational and intelligent systems, all with a strong interactive component. You will be part of Amsterdam Machine Learning Lab (AMLab). AMLab conducts research in machine learning
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real-world challenges faced by industry, governments, and society within the international STRUCTURE project? Information The PhD candidate will work within the international research project STRUCTURE