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Field
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for scent signals. Prior research experience and track record in signal detection, machine learning and deep learning. Prior programming experience in state-of-the-art AI techniques. Mastering of a
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
, financed by EU and national funds through FCT/MCTES (PIDDAC Workplan: Machine learning algorithms, in particular deep learning ones, suffer from the phenomena of catastrophic forgetting, which hampers
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interest and clerkship hiring and implement that learning within the department. Establish departmental standards for the review of fellowship project proposals and clerkship application materials. Develop
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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liaison with vendors/suppliers. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment Job Requirement Have relevant competence in the areas of Deep Learning
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required) Experience with machine learning / deep learning (PyTorch; model training; GPU workflows). Experience with Transformers / text embeddings / multimodal modeling (e.g., Hugging Face ecosystem
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applications, and presenting research at local, national and international forums. PhD supervision and public engagement experience is also desirable. You will bring deep expertise in ML/DL techniques, NLP
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safer, more efficient, and sustainable mixed-traffic future. Key Responsibilities: Design and conduct driving simulation experiments for risk scenario analysis. Develop deep learning models, such as GNNs
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to equity, excellence and impact. VU’s vision, outlined in our strategic plan Start Well Finish Brilliantly (2022–2028) , is to be a global leader in dual-sector research and learning by 2028. Our strategic