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an independent, proactive thinker who thrives in interdisciplinary collaboration, you are excited to contribute new insight into this understudied but ecologically highly relevant research field. Your
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high-density 3D Multi-Electrode Arrays (3D-MEAs) — done in close collaboration with experts, providing full support as you learn to analyze this data. o Multi-Omic Profiling: Performing and analyzing
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reaction kinetics to film formation and sealing performance, including controls against benign environmental changes and chemical interferents. Collaborate closely with the computational PhD and consortium
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pharmacy. In close collaboration with partners from industry, healthcare, and society, we contribute to the urgent challenges of our time, such as energy, sustainability, digitization, and medical technology
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than late in the utterance. You will learn to use multiple methods, including artificial language learning and EEG, collecting data from different language communities, including indigenous language
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Engineering, and Sustainable Production, Energy and Resources. We work on education and research in mechanical engineering, civil engineering and industrial design engineering. Together, we learn by making
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Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of the Leiden Institute
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) with computational methods. The candidate will obtain single-molecule multiplexing data and validate machine learning predictions using the high-throughput data. The successful candidate will collaborate
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, and the use of discarding low-quality entanglement (cut-off time). We will use simulation and analytical calculation for doing so. As part of the Doctoral Network collaboration his position includes
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projects using international cohorts and registries. Analysing data on reproductive outcomes, hormonal health, pregnancy and disease trajectories. Collaborating with international experts, clinicians