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to research infrastructure, and software for remote work. Selection process 11.03.2026 – 13.03.2026 – Submission of application files for the competition 16.03.2026 – Administrative verification, selection, and
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bioinformatics analysis pipelines for processing RNA-seq, single-cell RNA-seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when existing software packages
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bank transfer. Eligibility criteria Candidates must be enrolled in a PhD degree/programme in electrical and computer engineering, informatics and telecommunications engineering, or equivalent
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the consortium Your Profile: Excellent Master and subsequent PhD in computer science, engineering, biophysics, applied mathematics, computational biology or a related field Proven programming expertise in Python
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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers
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seeking a PhD candidate in computer architecture. The research focus of this PhD position is the design of performant and energy-efficient Edge Artificial Intelligence (AI) accelerators. Such accelerators
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methodologies tailored to the lab’s hardware/software testbed. Integrate SDRs (e.g., USRPs, RFSoC) and RF front-ends (FR2, FR3, V-band) into end-to-end ISAC demonstrators. Implement and optimize advanced signal
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optimization is essential. Where to apply Website https://www.academictransfer.com/en/jobs/359385/phd-on-multidomain-system-topol… Requirements Specific Requirements A master’s degree (or an equivalent
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FieldEnvironmental science » OtherEducation LevelBachelor Degree or equivalent Skills/Qualifications Expertise in equation oriented modelling with Matlab or Scilab. Process simulation in commercial software such as
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models