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advantage Experience with numerical methods in general, and particle tracking in particular, is an advantage Applicants must be able to work independently and in a structured manner and demonstrate good
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the following conditions: Scientific Area: Computer Science. Admission Requirements: Academic qualification: Master's degree in Computer Engineering, Computer Science, Electrical and Computer Engineering
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of local flora and native birdlife Basic knowledge of ecological processes and concepts Interest in birds and bird conservation Good verbal and written communication skills Comfortable doing field work
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, activities undertaken, and outcomes generated all consider the environmental consequences and, where possible, help to mitigate any impact. The position will be based within the School of Engineering at
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contextual scenarios. Develop a unified multimodal detection pipeline that integrates video, audio, and text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and
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to their host and antibiotics and to translate these insights to develop innovative approaches to prevent, treat and diagnose bacterial infections. In particular, we are interested in understanding: i) how single
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outputs, and nowcasting model products for hazard prediction workflows. • Develop and apply machine learning–based post-processing methods to enhance forecast skill for convective hazards. • Perform
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experiences on computer vision • Strong programming skills in using Deep Learning tools like PyTorch and GPU clusters. • Good written and verbal communications. • Open to Fixed Term Contract
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are a detail-oriented laboratory professional who enjoys precise, protocol-driven work and thrives as part of a collaborative research team. You bring sound technical knowledge, careful record-keeping
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other computer and data scientists, computational biologists, educationists and technologists. There are ample opportunities for various cross-domain ventures. Key Responsibilities: Deliver international