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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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written and spoken English skills High degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very
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, with three other modeling-focused PhDs who will work at different scales of assessment. This work is embedded into a larger team of PhDs, who are collecting data, working on multiple topics, from ecology
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between quantum computers (via Qiskit) and classical HPC resources Validate the QCS-MiMiC implementation on IBM’s ibm_cleveland quantum computer by reproducing recently published benchmark QM / MM
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning