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aims to unravel how ecosystems function in all their complexity, and how they change due to natural processes and human activities. At its core lies an integrated systems approach to study biodiversity
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. The research is broadly focused on automated intervention design. An intervention is any external interference in an ongoing process that is performed to achieve a particular outcome. Being able to answer
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of the selection procedure. Additional Information Benefits A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work
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relationships and collaboration with top academia, industry and research centres; the opportunity to contribute to the Φ-lab strategy and activities. As an internal research fellow within the Φ-lab, you will
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working in team settings including in transdisciplinary collaborations with scientists and policymakers. In our international working environment, most communication is in English (equivalent to language
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large language models (LLMs), natural language processing (NLP), and/or machine learning, with a verifiable track record (e.g., publications, thesis, or open-source contributions). Proficiency in Python
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research findings at leading machine learning and computer vision venues such as CVPR, ICCV, ECCV, NeurIPS, and ICLR; Present research at international conferences and workshops; Contribute
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vision, yet their ability to acquire new expertise remains limited when training data is scarce or specialised. This project aims to develop the next generation of adaptive visual learning systems by
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-disciplinary domain. Specific research topics to apply to bioacoustics might include: low-footprint machine learning; acoustic signal processing enhanced by ML; human-in-the-loop/active-learning methods
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-based and UV-curable inks. Implement FEM simulations linking chemistry, processing, and performance. Integrate experimental data from the experimental PhD’s for model validation. Collaborate with