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biotechnology, materials science, and electronics to tackle the key area of critical importance to society: healthcare and biomaterials. Parlak Lab investigates the application of bioelectronics in medicine
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of waveforms for satellite communication and radar systems communication/radar system performance analysis using theory and simulation field tests in relevant operating conditions retrieval of geophysical
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references where appropriate and be created without the support of generative AI. Copies of grades and diplomas. Copy of thesis/degree project. Contact details with e-mail addresses of at least two referees
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computational simulation. Join us in uncovering how complex organisms maintain their multicellularity during growth and development. Eligibility To fulfil the general entry requirements for studies at third-cycle
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application! We are looking for a PhD student in theoretical physics with a focus on the theory of magnetic materials. Your work assignments Your tasks will be to carry out research using advanced theoretical
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. Of particular interest is the investigation of compositional methods for constructing runtime monitors. The candidate will build on the latest advances in formal methods and learning theory, to develop methods
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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to
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, biophysics Machine learning and generative AI Molecular modeling and molecular dynamics simulations LNP formulation and characterisation including e.g. small angle scattering, microscopy, single particle
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML
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screening by exploring false negative and false positive outcomes of screening. Other projects we are working on are studies on blood-bound molecular markers of breast cancer using multi-omics approaches