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of “controllable” chemistries for creating and degrading linkages between bioactive semiochemicals and inactive precursory forms. This project will give you experience in bioassay-driven discovery and help develop
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environments, the use of machine learning frameworks (e.g. PyTorch) and version control tools (e.g. GitHub) is an advantage Applicants must be able to work independently and in a structured manner, and possess
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traversal tasks. Support calibration, perception, locomotion, manipulation, and control pipeline integration. ii. Experimental Testbed Development Design and set up representative door interaction
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. Strong competence in programming with C/C++ or Fortran for model development & debugging. Competence in software engineering best practices such as unit testing, use of modern version control systems
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. Develop and maintain reproducible research software, workflows and documentation using robust version control and good coding practices. Collaborate with QUBIC partners and interdisciplinary researchers
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control, reproducible experiments, 3D model management, documentation). • Support broader project goals as needed (e.g., dataset curation, method benchmarking, cross-validation of simulations with actual
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and contribute to peer-reviewed publications, conference presentations, and internal research reporting. • Maintain organized research workflows (version control, reproducible experiments
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of robotic locomotion systems. o Strong background in dynamics, compliant mechanisms, actuators and control for fin-propelled robots. o Proficiency with CAD, mechanical fabrication
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assignment of a research grant, with one position(s), under the project CENTRO2030-FEDER-02361900, title DA-TAC (Control of Alzheimer’s disease phenotypes with tacedinaline encapsulated in liposomes). The call
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closely with the principal investigator and collaborating researchers and will contribute to the following activities: Data organization, quality control, and preprocessing of plasma proteomic datasets