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Field
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the next generation of secure agentic AI systems through cutting-edge research in adversarial machine learning and formal verification. The Role As a research scientist, you will contribute to frontier AI
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in wireless communications and networking Background in AI and machine learning is an advantage. Experience and skills Knowledge of random-access protocols (e.g. IEEE 802.11 family). Understanding
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be a plus Education and training PhD in Bioinformatics or in Biology, Machine Learning, Statistics, Physics, Mathematics, Chemistry or related areas Languages: Highly proficient in both spoken and
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the development and/or implementation of algorithms and/or computational pipelines Background/experience in building statistical and/or machine learning methods, in particular for data integration tasks
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-processing system IMPROVER for blending probabilistic weather forecasts. The project will assess whether incorporating AI outputs and machine learning-based blending methods can enhance forecast accuracy
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and learning adaptation to uncertainty. You will use fMRI and neurostimulatory techniques (ultrasound neurostimulation and/or transcranial magnetic stimulation) to test the causal role of targeted
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society. Central themes of teaching and research include Digitalisation, Machine Learning, Autonomous and Intelligent Systems, Cybersecurity and Health Technology. The Faculty of Technology is the newest
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platforms include GRO-seq, RNA-seq, ChIP-seq, ATAC-seq, CRISPR-seq, single-cell RNA/ATAC-seq, microC, machine learning, sophisticated mouse genetic tools, and an ex vivo tissue culture system from patients
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PhD on top level in the field of architectural history and theory You bring founded experience in academic reserch and writing You bring appoved teaching experience from one or more academic
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profile PhD in Computer Science, Data Science, Machine Learning, or a related discipline. Proven experience in computer vision (e.g. image processing, deep learning, object detection, segmentation) and