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computing and artificial intelligence. Areas of interest include, but are not limited to, the following: Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks
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oxides, we will first develop and benchmark machine-learning interaction potentials of increasing complexity. Subsequently, we will deploy a combination of brute-force and rare-event sampling to isolate
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. Desirable experience in AI-assisted image analysis, machine learning, computational modelling, or building predictive models from biological imaging or cell–material interaction datasets. Strong experience in
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beginning in fall 2026, preferably with an interest or focus on statistical foundations of data science, artificial intelligence and machine learning, theoretical statistics and probability, uncertainty
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as natural experiments and interpretable machine-/deep-learning models. Publish research results in high-quality international journals (at least two peer-reviewed papers are expected). Eligibility
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, together with research experience in at least one class of modern generative models, are particularly encouraged to apply. In addition to conducting research, the successful candidate will teach two courses
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) develop multimodal machine learning models and methods to determine signatures and biomarkers to understand mechanisms distinguishing spontaneous versus precipitated withdrawal episodes. The spontaneous vs
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robotic middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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(AI) and machine learning (ML) methodologies. The position involves annotating clinical data, collaborating with AI/ML/NLP teams, developing algorithms, and generating insights to improve patient care