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processes. • Apply machine learning techniques and advanced statistical analysis to extract knowledge from complex datasets. • Participate in the evaluation and optimisation of high-performance scientific
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of computer literacy and knowledge of data management programs. Excellent organization skills, ability to prioritize a variety of tasks, and careful attention to detail. Ability to demonstrate professionalism
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be advantageous: Experience with scientific expeditions Experience in computer-aided analysis of biological sequence datasets (e.g., with R, Python and Bash/Linux environments) Basic understanding
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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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both Portuguese and English in the Apply UC computer platform and in the EURAXESS Portugal Portal, at https://www.euraxess.pt/ . Pursuant to paragraph h) of Article 9 of the Portuguese Constitution
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
or ideological beliefs, or trade union membership. Where to apply Website https://inegi.pt/pt/ Requirements Research FieldEngineering » Computer engineeringEducation LevelPhD or equivalent Research
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for using AI to develop social engineering attempts. This project combines human subject research of learning and decision making, Human-Computer Interaction, and the advancement in AI methods
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unique experimental platform developed in the Bakshi Lab (https://www.bakshilab.net/ ) that enables time-resolved measurements of individual bacterial cells throughout the phage infection process. The post
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, Microelectronics, Computer Engineering, or a closely related field, completed by the start of the position Have a solid background in digital hardware design: Verilog/SystemVerilog RTL, logic synthesis, and place
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applicability. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse