PhD Researcher in Physical Learning Machines
1 week geleden
AMOLF is seeking a highly motivated PhD researcher to join our Learning Machines group. The successful candidate will contribute to the development of fundamental understanding and theories regarding learning in physical systems, under real-world constraints. Our group aims to bridge knowledge gaps between computational and biological learning, as well as design physical learning machines that autonomously solve hard inverse design problems.
Key Responsibilities- Conduct research in physical learning machines, focusing on theoretical understanding and physically realizable learning rules.
- Develop and analyze new methods for diverse dynamical systems to learn, inspired by biological learning systems.
- Study the interplay of structure, interactions, and function in physical learning systems.
- Collaborate with international groups and companies to advance the field of physical learning machines.
- Strong background in physics, biophysics, electrical/mechanical engineering, materials science, or computer science.
- Excellent communication skills in English.
- Experience with coding (Python/Matlab) and numerical methods is advantageous.
- Familiarity with concepts in machine learning is a plus.
- A full-time appointment for four years, with a starting salary of €2,781 gross per month.
- Access to AMOLF resources, including computational models and experimental facilities.
- Opportunities for collaboration with international groups and companies.
- Support for housing, visa applications, and transport costs for foreign PhD students.
Interested candidates should submit their application, including a resume and motivation letter, via the online application button below.
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PhD Researcher in Physical Learning Machines
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PhD Researcher in Physical Learning Machines
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PhD Researcher in Physical Learning Machines
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Amsterdam, Noord-Holland, Nederland AMOLF VoltijdJob DescriptionWe are seeking a highly motivated PhD researcher to join our team at AMOLF, working on the development of fundamental understanding and theories regarding learning in physical systems. The successful candidate will be part of the Learning Machines group, led by Dr. Menachem Stern, and will focus on the theoretical understanding of physical...
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