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Field
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-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to
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these machine learning-based proxies together with a postdoctoral researcher working in this project (see below), leveraging data from experiments in our project. Third, you will explore how local connection
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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without neurons in physical systems, Ann Rev Cond Matt Phys14, 417 (2023) [4] Dillavou, Beyer, Stern, Liu, Miskin and Durian, Machine learning without a processor: Emergent learning in a nonlinear analog
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and presented at international AI, software engineering, and security venues. The position is focused on discovering new errors using behavioral models learned from software. You will extend fuzzing
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suggested patches, easier to understand using interpretable AI using state machine models and LLM-based explanations. You will provide the community with the first tool for self-healing software that is
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Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent years, practitioners and researchers have realized that predictions made by machine learning models should be
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this position, the chosen candidate will conduct research on how machine learning techniques or XAI can be leveraged by heuristic algorithms, or conversely, how heuristics can be enhanced by incorporating machine
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unique opportunity to contribute to the technological foundations for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential
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collaborative labs develop and deploy the latest technology, including sensing, data analytics, modelling, simulation, artificial intelligence, and machine learning, and function as dynamic hubs where innovative