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production. By developing hybrid architectures combining ontologies, generative models, reinforcement learning, and uncertainty quantification, the PhD project addresses the challenges identified by ICCARE in
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the intersection of statistical signal processing and optimization, with an emphasis on theory and algorithms. Key focus applications include array signal processing for sensing (e.g., radar, RF imaging, sonar) and
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with at least one ML framework (scikit-learn, transformers, etc.) · Familiarity with data structures and algorithms · Git proficiency for collaborative development Highly valued
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with the PI in the development of analytical strategies to achieve the laboratory goals, including optimization of current algorithms used to determine transcription factor binding dynamics, pseudotiming
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patients from public repositories (including dbGaP). Develop and apply machine learning algorithms to associate patterns in the data with cancer progression and therapeutic response in prostate cancer
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tasks (e.g. behaviour understanding and/or materials discrimination) across a range of imaging modalities. Specifically, they will investigate novel aspects of these tasks, develop software algorithms
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, focusing on the development of algorithms for proactive decision support in non-stationary warehouse environments. This project aims to study warehouse efficiency across interdependent processes – including
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in e.g. programming, algorithms and data structures, software systems architecture, use of AI, data acquisition and fullstack software-development. Following the Problem-Based Learning (PBL) model, you
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will contribute to high-impact projects, including: 1. Developing and validating algorithms that extract data from the Epic EHR (e.g., large language models) via comparison with manually extracted data
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.) · Familiarity with data structures and algorithms · Git proficiency for collaborative development Highly valued: · Previous work with LangChain, LlamaIndex, or similar retrieval and agent