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or related degree. Curiosity, resilience, and an interest in interdisciplinary, impact-driven research are essential. Experience with ecological fieldwork, data analysis (e.g., R or Python), or public
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on the topic (2,4). Training and Development Training will maximise future employability in academia and industry: Programming and geospatial data analysis using Python/R. Machine/deep learning techniques
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) Epidemiology, Bioinformatics or a related field; demonstrated expertise in programming and quantitative data analysis in R/Python; a strong interest in high dimensional omics and exposome research; excellent
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scientific computing or programming (e.g., Python). Knowledge of uncertainty quantification or inverse problems is welcome but not required. You have good verbal and written communication skills. You are
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dynamics analyses). You have solid programming skills, preferably in MATLAB and/or Python. You have a strong affinity for both musculoskeletal modeling and experimental research. You have good understanding
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data, including regression, classification, representation learning (e.g., spectra, micrographs, or time-series), and uncertainty-aware modelling for experiment planning. You are proficient in Python and
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a relevant subject (computer science, mathematics, or related subject) Proficiency in English (both oral and written) Python programming skills. Solid mathematical background is desirable. Studentship
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. Candidates must have proven ability to work with large datasets, coding with Python/Fortran/C++ and ideally experience with high-performance computing. Applicants from an industry background are encouraged
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, genetics, multi-omics data analysis, and/or genome annotations (R or Python skills desirable) • Prior experience in engineering Aspergillus spp. is a plus • Strong motivation for interdisciplinary research
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skills in scientific programming and quantitative analysis, preferably in python; affinity with climate science and/or policy modelling; excellent written and verbal communication skills in English