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knowledge in either (computational) Bayesian methods, or statistical learning for molecular data is required. Relevant research experience within unsupervised learning, or rank-based modelling / preference
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estimators, or machine learning) or other advanced statistical modelling. Advanced programming skills in Stata, R, Python or a similar software. Strong academic background with publications in international
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, or a closely related field. Proficiency in analysing large climate and environmental datasets using statistical or programming tools (e.g., Python, R, MATLAB). Capability of both managing research
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. Your main tasks will be Develop and apply machine learning techniques and statistical analyses, including novel methodology for analysis of complex polygenic traits and prediction tools for precision
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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studies. Proficiency in relevant computational tools and statistical methods. Experience with machine learning in large datasets. Interest and motivation to work in a multidisciplinary team. Ability to work
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a broad activity within physics with activities in solid state physics/material physics, soft/complex and biopolymer physics, medical physics, statistical physics, optics, atmospheric physics
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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also count in the assessment of the applicants: optogenetics, chemogenetics, computational and statistical methods for analysis; rodent behaviour, histology methods, sleep scoring, neurodevelopment as
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the following will also count in the assessment of the applicants: optogenetics, chemogenetics, computational and statistical methods for analysis; rodent behaviour, histology methods, sleep scoring