PhD Scholarship: Physical Machines: The Learning Machines group at AMOLF is offering exciting PhD positions for motivated individuals interested in delving into the realm of physical learning systems. As we venture to understand how materials can learn and adapt without traditional computation, we aim to bridge the gap between artificial and natural intelligence. Successful candidates will embark on projects exploring the theoretical foundations of physical learning, with opportunities to work on diverse dynamical systems and study the interplay of structure, interactions, and function. Join us in unraveling the mysteries of learning in physical and biological systems, and contribute to the development of novel learning machines capable of autonomously solving complex engineering problems.
PhD Scholarship Opportunity: Exploring Physical Learning Machines
Summary Table:
| Location | Amsterdam, Netherlands |
|---|---|
| Type | PhD positions |
| Eligibility | Candidates with backgrounds in physics, biophysics, electrical/mechanical engineering, materials science, or computer science |
| Strong verbal and written communication skills in English | |
| Experience with coding (Python/Matlab) and numerical methods preferred | |
| Terms | Full-time appointment for four years, starting salary of gross €2,781 per month |
| Assistance with housing, visa applications, and transport costs provided | |
| Contact | Dr. Menachem Stern |
| Email: m.stern@amolf.nl | |
| Phone: +31 (0)20-754 7100 | |
| Last Date | Rolling basis – Apply as soon as possible |
Location: Amsterdam, Netherlands
Eligibility/Qualification: We are seeking candidates with backgrounds in physics, biophysics, electrical/mechanical engineering, materials science, or computer science who possess a keen interest in learning in physical, biological, or computational systems. Applicants must hold or be working towards an MSc degree, and proficiency in English is required. Experience with coding (Python/Matlab) and numerical methods is advantageous, as is familiarity with concepts in machine learning. We welcome applicants from diverse backgrounds and perspectives.
Description: The Learning Machines group at AMOLF is dedicated to exploring the fundamental principles of learning in physical systems. Successful candidates will engage in theoretical and computational modeling of physically and biologically inspired systems, as well as the development and characterization of physical methods for learning. Projects may involve investigating how diverse dynamical systems learn and adapt in complex environments, or studying the interplay of structure, interactions, and function in physical learning systems. Through collaborative research efforts, candidates will contribute to the development of novel learning machines capable of autonomously solving challenging engineering problems.
How to Apply: To apply for this exciting opportunity, please submit your resume and a motivation letter (max. 1 page) detailing why you wish to join the Learning Machines group. Applications will be evaluated on a rolling basis, so we encourage interested candidates to apply as soon as possible. Please ensure your application includes your motivation letter, as it is an essential component of our evaluation process. Online screening may be part of the selection process. For further inquiries, please contact Dr. Menachem Stern via email at m.stern@amolf.nl or by phone at +31 (0)20-754 7100.
Last Date for Apply: Rolling basis – Apply as soon as possible.
