物理神经形态计算：新型材料与器件博士研究员
PhD Studentship: Physical Neuromorphic Computing: Novel Materials and Devices
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
AI 中文速览
- 研究内容
- 该DPhil项目探索下一代神经形态计算技术，利用物理系统的自然动力学进行类脑信息处理，重点研究自旋波动力学和二维自旋电子电路，结合实验器件开发、纳米加工、电磁表征与计算建模。
- 申请条件
- 申请人应具有或预计获得物理学、材料科学或紧密相关学科的强本科或硕士学位，对实验凝聚态物理、磁学、自旋电子学、纳米技术或神经形态计算有浓厚兴趣。
- 待遇
- 该博士奖学金由导师的英国皇家学会（Royal Society）资助经费支持，涵盖学费并提供标准生活津贴（stipend）。
- 申请方式
- 申请门户预计于2026年10月底开放，申请截止日期为2027年1月6日，项目于2027年10月开始的学年入学；请通过牛津大学物理系相关网页了解并提交申请。
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 截止
- （Europe/London） 剩 82 天
- 学科
- 物理与天文
- 合同类型
- 雇佣合同
- 原帖发布
- 本站收录
- 内容更新
- 原帖导师候选（待核验）
- Safeer Chenattukuzhiyil
- 来源
- jobs.ac.uk（英国博士项目及学术招聘） · 最近核对 2026-10-15
- 详情核验
判定依据（原文摘录）
- 这是一项博士（DPhil）培养机会
The DPhil project will explore next-generation neuromorphic computing technologies that exploit the natural dynamics of physical systems to perform brain-inspired information processing.
- 资助情况包括学费和标准生活津贴
The PhD studentship is funded through the supervisor's Royal Society grant, covering tuition fees and providing a standard stipend.
- 允许本科或硕士学位持有者申请
Applicants should have, or expect to obtain, a strong undergraduate or master's degree in physics, materials science or a closely related discipline.
原文
Overview
The DPhil project will explore next-generation neuromorphic computing technologies that exploit the natural dynamics of physical systems to perform brain-inspired information processing. The research will be undertaken within the MIND Research Group ( https://www.physics.ox.ac.uk/research/group/magnetism-intelligent-devices-mind ) at the Department of Physics, University of Oxford, under the supervision of Dr Safeer Chenattukuzhiyil. The PhD studentship is funded through the supervisor's Royal Society grant, covering tuition fees and providing a standard stipend.
Project Details
The rapid development of artificial intelligence (AI) has created an increasing demand for computing hardware capable of processing complex information with high speed and low energy consumption. Conventional AI systems rely on energy-intensive silicon processors, presenting growing challenges related to power consumption, scalability and computational efficiency. These limitations motivate the development of alternative computing architectures that exploit physical phenomena for information processing.
This DPhil project will focus on physics-based neuromorphic computing, where nonlinear interactions and collective dynamics in physical systems are harnessed to perform computation directly in hardware. The research will investigate spin-wave dynamics and two-dimensional (2D) spintronic circuits as platforms for developing compact, high-speed and energy-efficient neuromorphic devices.
The project builds on the MIND Research Group's recent work in physics-based neuromorphic computing, spanning real-time robotic control using nonlinear wave dynamics and the exploration of nanoscale spin-wave computing.
A major research direction will involve spin waves, collective excitations of magnetisation that propagate through magnetic materials. Their nonlinear dynamics, interference and interactions provide opportunities for physical reservoir computing and brain-inspired information processing. The research will investigate spin-wave-based devices and their application to computational tasks such as pattern recognition, temporal information processing and physical neural networks.
The project will also explore neuromorphic circuits based on two-dimensional van der Waals materials. These atomically thin materials provide opportunities to engineer spin transport, spin-charge interconversion and magnetic interactions within nanoscale devices. The research will investigate spintronic circuit architectures that exploit these properties to realise artificial neurons, synaptic functionalities and interconnected computing elements.
The research will combine experimental device development, nanofabrication, electrical and magnetic characterisation, and computational modelling to investigate the underlying physical mechanisms and demonstrate neuromorphic computing functionalities.
The overall objective is to develop new physical computing architectures that exploit spin dynamics and spin-dependent interactions, advancing energy-efficient neuromorphic hardware for artificial intelligence, robotics and intelligent sensing.
Person Specification
Please check whether you are eligible to apply for the University of Oxford Physics DPhil programme before applying or contacting the supervisor.
We seek a highly motivated candidate with a strong interest in experimental condensed matter physics, magnetism, spintronics, nanotechnology or neuromorphic computing. Applicants should have, or expect to obtain, a strong undergraduate or master's degree in physics, materials science or a closely related discipline.
Candidates should demonstrate strong analytical and problem-solving skills, enthusiasm for experimental research, and the ability to work independently and collaboratively. Previous experience in magnetism, spin dynamics, nanofabrication, electrical measurements, 2D materials, programming and machine learning would be advantageous. Candidates should also be able to work effectively both independently and as part of a collaborative research team and communicate scientific results clearly.
Application Procedure
The position will start in the academic year beginning in October 2027. The application portal expected to open by the end of October 2026, and application deadline is 6 January 2027.
All candidates should check their eligibility and review the application procedure before applying. For further details, please visit:
https://www.physics.ox.ac.uk/research/group/magnetism-intelligent-devices-mind/join-group