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Develop, validate, and document ISAC channel modeling 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
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algorithms from technical sources (e.g., papers, notes, and group discussions) in close collaboration with group members. The work is primarily algorithmic scientific software development: implementing and
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candidates must thus have a strong interest in algorithmic development as well as embedded hardware integration. Role and responsibilities This PhD project will be executed in close cooperation with
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discovery. While deep generative models have shown promise in proposing novel molecular structures, they typically require massive, cleanly labelled datasets to train effectively. In practice, acquiring high
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data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems, which can benefit a wide range of real-world
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from astrometry, flat-field determinations, PSF uncertainties, and gain drifts. The applicant will also develop algorithms for quantifying best-fit flux and errors with outlier rejection in the all-sky
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Networks” and is to be delivered by the quantum discovery of science group led by Prof Oleksandr Kyriienko. The research will involve designing understanding and developing innovative approaches to graph
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considerations and algorithms for protocol documents according to study design and appropriate statistical methods, manage and maintain documentation of files and analyses. This person will summarize and present
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for application to fusion reactors, considering factors such as muon flux, detector sensitivity, and data analysis algorithms. PhD candidates will be tasked with developing simulations of complete detectors and
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analysis pipeline and algorithms development and validation. 3. Extracts features of interest from data relating to specific biological questions. 4. Contributes to the development and maintenance of genomic