INFORMATION ABOUT THE POSITIONPosition:PhD studentResearcher Profile:First Stage Researcher (R1- up to the point of PhD)Number of vacancies:1Project:ReNeMos: Recurrent Network Models of Statistical LearningLocation:Spain > San SebastianResearch Field:NeuroscienceType of contract/Duration of Contract:Temporary: 4 yearsJob Status:Full-timeHours per week:35Starting date:January 2025 (flexible)Application deadline:15 November 2024Information about the project:Statistical learning (SL) is a crucial capacity supporting efficient cognition and action throughout the lifespan.
SL is an organism's ability to attune to coherent covariation in its environment (e.G., an infant learning sound patterns of a language, or visual patterns corresponding to objects).
This project uses recurrent neural network modeling to investigate the types of computations that may support human SL.
The modeling motivates EEG and MEG studies with human subjects.
Together, the modeling and experiments will advance our understanding of the nature and computational basis for human SL.Job description:Collaborating on finalizing experimental designs, implementing them, and collecting dataAnalysing EEG and MEG dataWriting research papers under the supervision of the PI, aiming to publish at top-tier journalsDissemination of results at international scientific conferencesPI and research group:Dr. James Magnuson will be the supervisor of this PhD project.
Magnuson leads the Computational Neuroscience group at BCBL.
Our group is developing novel computational approaches focused primarily on typical and atypical learning and language.
Our general approach is to use modelling to motivate new hypotheses we then test with human subjects, in order to advance theoretical understanding.2.
CANDIDATES' PROFILE AND SELECTION CRITERIARequired skills:Good knowledge of cognitive science/neuroscienceMaster's (or equivalent) degree in Psychology, Computer Science, Cognitive Neuroscience, Linguistics, or a related fieldExcellent written and oral communication skills in EnglishDesirable skills:Programming experience is highly desirableExperience with neural networks is also highly desirablePrevious experience in participating in research projects (e.G., data collection, analysis)3.
WORKING CONDITIONSSalary:21,000€/year gross on average across the four years of the contractTraining opportunities and Career development plan:Researchers at any stage of their career, regardless of their contractual situation, are given opportunities for professional development and for improving their employability through access to a Personal Career Development Plan which includes:Training through individually personalized research projects under senior supervisionExchanging knowledge with the scientific community and the general publicNetwork-wide training in theory and methodsComplementary training coursesInvolvement in proposal writing, task coordinationDevelopment of skills for the organization of training and scientific eventsBCBL seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments.
If you have any questions relating to accessibility or support contact us.4.
OTHER RELEVANT INFORMATION:Language policyThe corporative language at the Center is EnglishThe center provides initial level Spanish and Basque lessons to all the international staff membersThe interview will be conducted entirely in English5.
APPLICATION PROCESS:Submission of the application and documentation:To submit your application, applying for "Ph.D.
CANDIDATE POSITION – RECURRENT NETWORK MODELS OF STATISTICAL LEARNING" and attach the following documentation:A curriculum vitaeA statement outlining research interests and motivation to apply for the positionTranscript of records for the completed master's and bachelor's degreesTwo letters of recommendationLearn more about the BCBL's OTM-R policyApplication process timetable:Deadline for application: 15/11/2024Evaluation by committee: 18/11/2024-27/11/2024Interviews: 02/12/2024-05/12/2024Final decision: 06/12/2024Feedback to all applicants: 10/12/2024Work contract start date: 15/01/2025 (flexible)Contact details for enquiries: ******#J-18808-Ljbffr