The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The selected candidate will expand the computational modeling framework Cobel-RL and use it to study how episodic memory might be used to learn to navigate.

The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The group is actively seeking a talented graduate student to join the team, who will expand the computational modeling framework Cobel-RL (https://doi.org/10.3389/fninf.2023.1134405) and use it to study how episodic memory might be used to learn to navigate.

Doctoral Position in Computational Neuroscience. Are you curious about how the human brain stores memories? Have you wondered how we manage to navigate through space? Our dynamic research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. Currently, we are actively seeking a talented graduate student to join our team, someone who will expand our computational modeling framework Cobel-Spike and use it to study how spiking neural networks can learn to navigate. This position is 65% at TV-L E13, starts as soon as possible, and is funded for 3 years.

The Wiring, Neuromodeling and Brain Lab at IT:U Interdisciplinary Transformation University Austria is looking for two PhD students to work on neuromodulation-aware artificial intelligence. We are interested in (1) the role of individual neuromodulators (e.g., dopamine, serotonin, and acetylcholine) in initiating and implementing diverse biological and cognitive functions, (2) how competition and cooperation among neuromodulators enrich single neuromodulator computations, and (3) how multi-neuromodulator dynamics can be translated into learning rules for more flexible, robust, and adaptive learning in artificial neural networks (ANNs). Start date: Jan-Mar 2025. Apply by Nov 30, 2024: https://it-u.at/en/research/research-groups/computational-neuroscience/ For more information, please visit: https://majorjiemei.wixsite.com/wnblab If you have any questions, please contact Dr. Jie Mei (jie.mei@it-u.at).