材料科学博士职位
PhD Studentship: Calibration Methodologies for Industrial/Geophysics Granular Materials
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
- 该项目致力于开发强大的方法来选择和参数化接触模型，使用高级AI技术，包括机器学习、AI驱动的模型选择和深度学习。
- 申请条件
- 至少2.1荣誉学位或硕士（或国际同等学历）在相关科学或工程学科。
- 待遇
- £21,805 每年的税前津贴和学费将被支付。
- 申请方式
- 请联系Dr Anthony Thornton - Anthony.Thornton@manchester.ac.uk，包括当前学习水平、学术背景、相关经验和研究动机。
- 材料清单
- 当前学习水平
- 学术背景
- 相关经验
- 研究动机
由 @cf/meta/llama-3.3-70b-instruct-fp8-fast 生成，博士岗判定置信度 90%。
结构化信息
- 截止
- （Europe/London） 剩 5 天
- 学科
- 材料科学
- 合同类型
- 雇佣合同
- 原文薪资
- GBP 21,805 / 年（税前）
- 税后月薪（估）
- ¥13,700；房租后 ¥5,400
- 估算假设
- 单身、无子女、雇佣合同的粗略估算，以学校 offer 为准；扣除率 18%；汇率日期 2026-10-01
- 原帖发布
- 本站收录
- 内容更新
- 导师
- Dr Anthony Thornton
- 来源
- jobs.ac.uk（英国博士项目及学术招聘） · 最近核对 2026-10-07
判定依据（原文摘录）
- is_phd
This 3.5-year PhD project
- english_ok
We recommend that you apply early as the advert may be removed before the deadline.
- bachelor_ok
Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent)
原文
This 3.5-year PhD project is fully funded and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.
We recommend that you apply early as the advert may be removed before the deadline.
This PhD project addresses the "calibration problem" in particulate continuum models and particle simulations. Specifically, it focuses on developing robust methodologies for selecting and parameterising contact models, a crucial but challenging task due to the lack of standardised measurement techniques. The research will explore and refine "indirect" or "bulk" calibration methods, using characterisation machines to match simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty quantification will enhance the reliability of calibrated parameters. Overcoming challenges like dimensionless indices, varying machine types across disciplines, and multi-parameter dependencies, the project aims to establish improved, AI-enhanced calibration strategies for diverse industrial and geophysical materials. Ultimately, it seeks to determine the optimal, AI-informed approach for selecting and calibrating discrete particle models for specific materials.
Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.
To apply please contact Dr Anthony Thornton - Anthony.Thornton@manchester.ac.uk . Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project. £21,805 annual tax-free stipend set at the UKRI rate and tuition fees will be paid