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Job Description The Institute of Mechanical and Electrical Engineering at SDU invites applications for a PhD position in Neuromorphic Brain-Computer Interface Design. Are you a multidisciplinary
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-computer interfaces, cognitive rehabilitation, and neural prosthetics. Your contributions will support the development of a custom CMOS-based SNN processor that can operate in ultra-low-power environments
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well as the organic matter persistence. Responsibilities and qualifications Your tasks will be to: Use bioinformatic tools to mine metagenomic datasets for enzyme-specific sequences (oxidoreductases and/or hydrolases
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biology, or analytical workflows. Interest in single-cell analysis, cancer biology, and translational research. Basic level expertise in computational biology (e.g., bioinformatics, machine learning), with
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for intelligent brain-computer interfaces? We are offering a PhD position in analog/mixed-signal CMOS circuit design for EEG and wearable sensor interfaces, as part of a pioneering project focused on assistive
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of extracellular matrix (ECM) remodelling during wound healing. Yet, many proteolytic processes remain uncharacterized due to the complexity of the ECM and limitations in current detection strategies. Advances in
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PhD fellowship at the Copenhagen Center for Glycocalyx Research at the Department of Cellular and Mo
data science, bioinformatics, protein design, biochemistry, mass spectrometry, cell biology, molecular biology, genetic engineering, medicine or related fields. The successful candidate will join a
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Job Description Are you passionate about artificial intelligence, bioinformatics, food safety, and creating real-world impact through cutting-edge technology for improving food safety? Do you want
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small-scale processing sector. By joining this project, you will contribute to the development of AI-powered tools that predict non-compliance, improve food safety monitoring, and ultimately protect
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diffusion techniques to design materials with targeted optical properties, scaling to large systems through efficient representations and GPU parallelization. We will also create multi-fidelity predictive