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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 1 month ago
networks or similar machine learning technologies applied to DNA; Preferential: Experience with transcription factor motif discovery; Proficiency in high-throughput sequence alignment methods; Candidates who
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qualifications and/or diplomas, if applicable. Enrollment in a non-degree course developed in association or cooperation between a High Education Institution and one or multiple research units. Work plan
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data (sequences and associated properties), adapting existing generative architectures (e.g. VAEs and diffusion-based models when appropriate) to the peptide domain, and defining suitable representations
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improvements in classification stability and security-aware code generation. The work plan includes: (Month 1) Implement contrastive learning fine-tuning using a tailored Multiple Negatives Ranking Loss (MNRL
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-induced variations. The work plan includes: Evaluation Pipeline Development (Month 1) Implement a scalable evaluation framework using local LLM infrastructure (e.g., Ollama, LMStudio). Integrate multiple
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of appropriate genetic markers, and preparation of samples for high-throughput sequencing; c) Management, pre-processing and analysis of high-throughput sequencing data obtained through DNA metabarcoding
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of contractualization. Possess advanced knowledge and competencies in the field of health sciences, such as neurology and/or neurobiology, aligned with Level 7 of the European Qualifications Framework (EQF). Meet at
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, environmental psychology,geography, or related fields);• Hold an academic background aligned with the research topic;• Preferably have skills in research methods applied to urban contexts and interest in
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, thereby advancing technological solutions in the field of biosensors. Specifically, the objectives are: • To design and specify a robust and scalable technological solution aligned with the current
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specify a robust and scalable technological solution aligned with the current challenges of intelligent biosensor-based monitoring; • Develop software components for real-time data collection, processing