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Equations) Programming and data analysis in Python Language requirement: Good oral and written communication skills in English English requirements for applicants from outside of EU/ EEA countries and
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. Desired qualifications: Strong theoretical and methodological capacities in statistics Documented expertise in computational methods and experience with R or Python programming Experience in Bayesian
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background in statistics (a minimum of 60 ECTS in statistics). Knowledge in programming skills (e.g., R, Python). Candidates without a master’s degree have until June 30, 2026 to complete the final exam
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defense are eligible for appointment. Programming skills in GAMS and Python. Experience with HPC. Experience with
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interest and solid foundational knowledge in at least one of the research projects Proven competence in computational design workflows, particularly in Rhino and Grasshopper Knowledge of Python, C#, finite
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knowledge of handling sequence data from RNA-seq or other NGS-based approaches. Experience with data analysis in Python/R. Experience with experimental work on plants or other eukaryotic organisms in
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packages relevant to biostatistics and bioinformatics (e.g. R, Python, Fortran, Matlab, SAS, Stata or equivalent) and experience in the use of high-performance computer resources and parallel computation
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in programming with languages commonly used in AI development (e.g., Python, R, Java, C++, JSON). Experience in designing, developing, and evaluating AI/Machine Learning models. Excellent analytical
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technologies, is highly desirable. Skills: Strong knowledge of clinical informatics frameworks, standards, and methodologies. Proficiency in data analysis software (e.g., R, Python, SAS, SPSS, SQL) and
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and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models 2. Hold at