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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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related field Solid understanding of machine learning, especially deep learning and transformer models Practical experience with Python and ML frameworks (e.g., PyTorch, HuggingFace, NumPy, sklearn) Basic
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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partnership between academia and industry to drive research and development forward. Project description This project aims to develop unsupervised machine learning methods for extracting dynamical models
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Qualifications: PhD in computational genomics Experience with: • Computational and bioinformatics • Machine learning and statistical modeling • Programming and data infrastructure • Experimental and Field Methods
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
, and real-time classroom monitoring. Design and implement machine learning models to analyze multimodal data (e.g., student behavior, engagement, and performance) to enhance personalized learning
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, the identification of predictive features, and the construction and validation of statistical or machine-learning-based models. The postdoctoral researcher will be responsible for: Developing a
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, Computer Science, or related field Preferred Experience: Experience with machine learning in medical imaging/biomechanics; grant writing support; clinical gait analysis in clinical/research setting; gross anatomy
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date