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with a background in Biotechnology/Cognitive Psychology and Mental Health, who can demonstrate interest and knowledge on the topics of Cognitive Conflict, Mental Effort, and Emotions and on the methods
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be in charge of finishing ongoing projects in the group, involving the development of probabilistic methods for the identification of non-coding cancer driver elements and of the tumor mode of growth
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language: Java). A fundamental background in machine learning. Ability to write high-quality scientific papers (e.g., well-graded theses, publications). Experience with quantitative methods (e.g., agent
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ideological and theological messages, especially in contexts of low literacy and absence of media. Using the most comprehensive dataset of visual art in Italian churches, combined with computer vision methods
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single-cell genomics, chromatin profiling, and comparative genomics methods in a phylogenetically diverse array of eukaryotes. For more details about the lab, visit the lab website: https
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Research and Innovation 2024-2027. 1. First project in which the successful applicant will collaborate: 1.1. Name of the project: “Water cycle characterisation and GeoSAR data estimation methods”. 1.2
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and translating it into actionable guiding steps. Methods will include literature reviews; qualitative and mixed-methods research; participatory co-creation with clinicians, patients, developers and
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-signal circuits. - Valuable Knowledge of comercial design tools like Cadence / Synopsys. - Competence in computer architectures and digital system design with HDLs (Verilog or VHDL). - Knowledge
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the academic requirements necessary for admission to the University of Alicante PhD programme in Computer Engineering (Spanish MECES level 4 or EQF level 8). Proof of admission to the doctoral programme
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of synthetic toggle circuits, guided by time-course, single-cell resolution data. These models will then be integrated with automated biocircuit design tools, synthetic biology methods, and strain development