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Field
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in laboratory work and materials characterization techniques (e.g. SEM, ICP-MS) Basic knowledge of thermodynamic modelling or willingness to learn tools such as FACTSage Analytical mindset with strong
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combining analytical and numerical approaches to model realistic quantum experiments and appreciate how the two complement one another. You are eager to learn new techniques and explore unfamiliar ideas
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, from resources to the end-user. We look at how energy is produced and used by humans and machines in a sustainable way with regard to health, climate change and the resource base. The Department
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placements (honours, master degree, PhDs) and create a community of students who will learn together as they tackle these research challenges. More information including available projects and how to apply
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using R and/or Python, with experience in data analysis, computational workflows (e.g. Snakemake, Nextflow), and relevant computational libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow/PyTorch
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, pandas, scikit-learn, TensorFlow/PyTorch, or equivalent) is a strong advantage Experience with high-throughput molecular assays, particularly next-generation sequencing (NGS) data generation and/or
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, their empirical security assessment remains underdeveloped, representing a growing and critical gap. Recent studies have highlighted vulnerabilities in learning‑enabled CPS, demonstrating that small, carefully
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skills in English. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Willingness to learn new fields and adapt to a multidisciplinary research environment
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● Electrical and Computer Systems Engineering ● Materials Science and Engineering ● Mechanical and Aerospace Engineering For further information, please follow the link for each Graduate Research Degree ● 3291
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learning programme to foster our staff’s soft and technical skills Multicultural and international work environment with more than 60 nationalities represented in our workforce Diverse and inclusive work