PhD student in computational modeling of highdimensional visual category le at Ruhr Universitt Bochum
44801 Bochum, , Germany -
Full Time


Start Date

Immediate

Expiry Date

31 Aug, 25

Salary

0.0

Posted On

01 Jun, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Working Environment, Job Applications, Disabilities

Industry

Education Management

Description

COGNITIVE NEUROBIOLOGY

In order to fill a fixed-term position in part-time (26,6861 hrs/week = 67%) at the earliest possible date, we are looking for 1

PHD STUDENT IN COMPUTATIONAL MODELING OF HIGHDIMENSIONAL VISUAL CATEGORY LEARNING (M/F/X)

The lab seeks to understand the cortical basis and computational principles of perception and experience-dependent plasticity in the brain. To this end, we use a multimodal approach including fMRI-guided electrophysiological recordings in rodents and non-human primates, and fMRI and iEEG in humans. See https://doi.org/10.1016/j.cub.2023.01.011 and https://doi.org/10.1038/s41467-024-51543-y for recent examples of our work. The PhD student will play a key role in our research efforts in this area. The lab is located at Ruhr-University Bochum (https://www.rub.de) and the German Primate Center (http://www.dpz.eu). At both locations, the lab is embedded into interdisciplinary research centers with international faculty and students pursuing cutting-edge research in cognitive and computational neuroscience. The PhD student will have access to dedicated computing infrastructure, including high performance computing clusters. The project will be conducted in close collaboration with the labs of Fabian Sinz (https://sinzlab.org), Alexander Gail (https://www.dpz.eu/sensomotorik), and Igor Kagan (http://igorkagan.org). The main site for this part of the project will be Bochum. Ruhr-University Bochum provides a vibrant and stimulating neuroscience community with a strong community in computational as well as experimental neurosciences. The PhD student will have the opportunity to join the International Graduate School for Neuroscience (https://www.ruhr-uni-bochum.de/igsn/index.html) or the International Graduate School of Biosciences (https://www.biologie.ruhr-uni-bochum.de/biodek/promotion/index.html.en).
The Department of Cognitive Neurobiology of Caspar Schwiedrzik at Ruhr-University Bochum is looking for an outstanding PhD student with expertise in computational cognitive neuroscience and/or deep learning to join our team to the neural basis of mental flexibility. The project investigates neural mechanisms of high-dimensional visual category learning, utilizing computational approaches as well as neuroimaging and neurophysiology in humans and macaque monkeys, respectively. It is funded by an ERC Consolidator Grant (Acronym DimLearn; “Flexible Dimensionality of Representational Spaces in Category Learning”). The PhD student’s project will focus on developing deep learning “digital twin” models based on neural and/or behavioral data to investigate flexible multi-task learning.

THE RUHR-UNIVERSITÄT BOCHUM IS ONE OF GERMANY’S LEADING RESEARCH UNIVERSITIES, ADDRESSING THE WHOLE RANGE OF ACADEMIC DISCIPLINES. A HIGHLY DYNAMIC SETTING ENABLES RESEARCHERS AND STUDENTS TO WORK ACROSS THE TRADITIONAL BOUNDARIES OF ACADEMIC SUBJECTS AND FACULTIES. TO CREATE KNOWLEDGE NETWORKS WITHIN AND BEYOND THE UNIVERSITY IS RUHR-UNIVERSITÄT BOCHUM’S DECLARED AIM.

The Ruhr-Universität Bochum stands for diversity and equal opportunities. For this reason, we favour a working environment composed of heterogeneous teams, and seek to promote the careers of individuals who are underrepresented in our respective professional areas. The Ruhr-Universität Bochum expressly requests job applications from women. In areas in which they are underrepresented they will be given preference in the case of equivalent qualifications with male candidates. Applications from individuals with disabilities are most welcome.

Responsibilities
  • Develop cutting-edge digital twin models for category learning from neural and behavioral data
  • Analyze model outputs to test theoretical frameworks about visual category learning
  • Contribute to the development of a toolbox for digital twin modelling
  • Work closely with experimental team members in DimLearn to validate computational predictions
  • Presentation and publication of results through conference contributions and per-reviewed scientific publications
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