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(e.g. fuel cells, batteries, heterogeneous catalysts, interfaces) Proficiency in high-level programming languages, e.g. Python Previous supervision of graduate students, as well as laboratory
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: Proficient in Python. Machine Learning: Strong experience with frameworks like TensorFlow, Keras, or PyTorch. Preferred Skills: Experience with Explainable AI (e.g., SHAP, Integrated Gradients
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of mRNA or expression libraries lead in vivo studies, including delivery, tissue collection, phenotyping, and downstream analysis integrate and analyse multimodal datasets using Python or R to inform
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QUALIFICATIONS: PhD (or equivalent) in biology, applied mathematics, statistics, computer science or related field. A proven track-record of peer reviewed publications, and programming experience in Python or R
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, knowledge and skills in using advanced statistical methods (e.g., HLM, SEM, non-linear dynamical systems) and programming skills (e.g., R, Python, MATLAB) as well as data mining techniques for analyzing
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, problem solver, and programming skills for Python and Matlab are preferred Experience in similar environments with industrial collaborations or public-funded research projects is highly desirable Fluency in
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methodological training in (bio)statistics and/or machine learning Interest in developing rigorous methods for biomedical, clinical, or public health data Experience with statistical computing (R and/or Python
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to apply. Basic to advanced experience with computational analysis and R/Python. Excellent oral and written communication skills and be able to work effectively with collaborating researchers. Successful
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software (e.g., WaterTAP) and programming languages (e.g., Python) is highly desirable. Familiarity with the principles of electrified mineral extraction and purification processes is advantageous but not
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structure theory software packages, including but not limited to Core Level, Infrared, and Raman Spectroscopy. Language can be anything of the candidate’s preference, such as Python, Fortran etc. Leverage