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                responsibilities for the Research Assistants are: Learn psychophysiology data management and processing methods Implement data management, preparation, and processing methods with participant files with detail 
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                to work with an international team on developing cutting-edge novel demographic, statistical and computational methods in estimating, modelling and forecasting measures of health, well-being, and human 
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                methods, parameter estimation, and data visualization. Some knowledge of probability and statistics. Other Requirements: CV Research Statement 3 Reference Letters Cover Letter Full-time, 12-month position 
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                . Selection methods and criteria IV.1. Selection Methods: Assessment of scientific background and CV [ABC (90%)] and Interview [I (10%)]. IV.2. In the assessment of the candidate’s background and CV, scientific 
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                learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen 
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                the occupation of the positions or, when the positions cannot be totally occupied, due to the inexistence or insufficiency of candidates to continue the contest. IV. Selection methods and criteria IV.1. Selection 
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                of the Consolidated Text of the Workers’ Statute Law. 2.6. Estimated duration of the contract: up to 36 months. The University of Alicante will enter into a contractual relationship with the successful applicant 
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                Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta 
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                required. Consequence of Error/Judgement Exercises a considerable amount of judgment, responsibility, and initiative in determining work procedures and methods, and coordinating the work of the unit 
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                the limitations of traditional training methods using a deliberate practice approach, VSWT enhances the consistency and quality of social work practices. This innovative approach addresses critical gaps in