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Offer Description We are looking for a PhD student to join our Signal Theory and Statistical Learning group at TU Darmstadt, Germany. The position is in the mathematical foundations of deep learning, with
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PhD Studentship: Robust, Certified, and Scalable Federated Machine Unlearning for Privacy-Preserving AI About the Project As federated learning systems become increasingly embedded in high‑stakes
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. We envisage combining process-based modelling and machine learning, as well as integration of Earth observation data into the modelling framework. The objective is to quantify the GHG budgets
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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learning, or human-computer interaction would be advantageous. How to apply We are seeking expressions of interest from qualified domestic candidates who wish to apply for this PhD opportunity. This position
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for probabilistic unsupervised learning for structured biological data. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/307053/3-years-phd-position-in-probabilistic-machine
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, including highly polar, persistent, mobile and fluorinated compounds, in wastewater, sludge and advanced treatment systems to support machine-learning models for contaminant fate and removal; and large-scale
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learning models. Strong background in machine learning optimization, with experience in distributed or multi-node collaborative training; experience in large-scale training is highly desirable. Strong
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analysis, or assistive technology; Experience in applying AI methods such as machine learning, deep learning, computer vision, multimodal data analysis and large language models (LLM) in health-related
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applications Qualifications Requirements PhD in a relevant field such as Artificial Intelligence, Machine Learning, Computer Science, Computational Science or other field that the employer finds relevant