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and phylogenetic comparative analyses using R or Python Present research findings at scientific meetings and symposia Prepare and contribute to the publication of results in peer-reviewed journals Your
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deep learning, generative models, or biomedical imaging/omics data Strong programming skills (Python, PyTorch, etc.) A commitment to interdisciplinary collaboration and impactful research How to Apply
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placement locations. Advanced metaheuristic optimisation algorithms, implemented in Python and interfaced with DIgSILENT PowerFactory, will be developed to determine the most effective PST locations and
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but not mandatory). A strong interest in materials simulation, quantum materials, and condensed matter physics. Experience with Python. Fluency in English is required. Finnish language is not required
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experience of data science analytical packages and R or Python would be advantageous. Funding The project is funded by the UKHSA and Nottingham University Business School. As a UK public body, UKHSA can only
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of AI/ML will be an added advantage. Proficiency with optimization tools and software like GAMS, CPLEX Proficiency with programming languages preferably Python, Matlab Fluency in communication and
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into longitudinal studies and in using functional MRI and scripting-based languages (e.g. Matlab, R, Python). They must also have experience of conducting research with human participants and of performing highly
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analyse large, multidimensional 4D STEM datasets. Develop or adapt software tools (e.g. Python, MATLAB) for image reconstruction, phase mapping, and quantitative analysis of ferroelectric domain wall
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experience with programming tools such as R or Python. An interest in acquiring new data science and programming skills is required. A keen interest in teaching and academic development. An excellent academic
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include: Developing the model using open-source Python software Planning and conducting experiments Analysing teardown reports and experimental data Validating and improving the model Publishing results in