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Hosting Institute		Details of position
ISTC-CNR Rome Italy		What: Master thesis (Italian: Tesi universitaria di primo e secondo livello), or PhD Research topics: Cumulative learning in humanoid robots based on intrinsic motivations Start: now Opening of call: full duration of project, see below Duration: 1 year Funding: candidate own funds; PhD bursary found by candidate in other institutes. Contact: gianluca (dot) baldassarre {at} istc (dot) cnr (dot) it How to submit an application:

The position is in the context of the four year, eight partner EU-project, IM-CLeVeR (Intrinsically Motivated Cumulative LEarning VErsatile Robot), which spans neurobiology, psychology, computational neuroscience, machine learning and robotics (http://www.im-clever.eu/). Our goal will be to develop algorithms that learn hierarchical control architectures based on intrinsic motivations, curiosity drives, and abstraction abilities.

Candidates should have a keen interest in the areas of computational neuroscience, unsupervised and reinforcement learning, control theory, intelligent robotics, or related disciplines.

The positions will be based at the ISTC-CNR Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, Roma (http://www.istc.cnr.it/) within the reserch group LOCEN - Laboratory of Computational Embodied Neuroscience (http://locen.istc.cnr.it) under the supervision of Dr. Gianluca Baldassarre (http://laral.istc.cnr.it/baldassarre/) and Dr. Marco Mirolli (http://laral.istc.cnr.it/mirolli).

Positions are available immediately, but rely on the capacity of the candidate to self-fund, e.g. by winning  a PhD bursary from EU or universities. The research work can benefit of the facilities of the project, including labs, computers, and a iCub humanoid robot.

If interested, please send Gianluca Baldassarre a curriculum vitae comprising this information:

• Name, age, age, living address, nationality
  
• Undergraduate studies with topics and marks
• University exams to pass, if any, and expected time of completion
• Time when available to start the research
  
• Previous experiences of research
  
• Scientific interests
• Programming skills
  
• English competence
• References of publications, if any
  

The position is in the context of a new four year, eight partner EU-project, IM-CLeVeR (Intrinsically Motivated Cumulative LEarning VErsatile Robot), which spans neurobiology, psychology, computational neuroscience, machine learning and robotics (http://www.im-clever.eu/). Our goal will be to develop algorithms that learn hierarchical control architectures based on intrinsic motivations, curiosity drives, and abstraction and modularization abilities.

and should be sent to: schmitz@fias.uni-frankfurt.de. 

Hosting Institute		Details of job position
University of Sheffield Sheffield UK		What:  Postdoctoral Research Associate Research topics: Computational Neuroscience, Intrinsically Movtivated Learning Start: June 2010 (position filled) Opening of call: March 2010 Duration: up to three years Opening of call: Contact: Professor Kevin Gurney How to submit an application:

This post, now filled, is associated with the multinational and interdisciplinary project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots - funded by the European Union. We are seeking someone to work in the area of computational neuroscience developing models of intrinsically motivated learning. One main focus of the work is the development of biologically constrained models of a novel learning task using a joy-stock and computer generated stimuli. As such, the successful candidate will work closely with an experimental psychologist at Sheffield who is investigating human performance on the new task. The IM-CLeVeR project links with the wider programme of computational neuroscience conducted within the Adaptive Behaviour Research Group (ABRG - www.shef.ac.uk/~abrg) and the appointee will become part of its vibrant and successful research team. Candidates will possess a PhD in computational neuroscience, or related area, or equivalent.