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丹麦技术大学 · 数学建模与计算硕士 硕士

Technical University of Denmark|Mathematical Modelling and Computation。 下面的学制、学费、入学时间、语言要求与课程设置均来自该校该项目本身,不与其他学校同一名称的项目共用。
QS 2027 · 第 105 位
学位 硕士 · 75 门课程

📖 项目简介

Mathematics is an integrated part of our everyday lives. It is found in mobile phones, train schedules, and online search engines—to give just a few examples.

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The use of mathematics will become even more prevalent in future. There is an ever-increasing demand for mathematical models and methods in, e.g., industrial applications, information technology, financial institutions, energy, government issues, drug development, DNA mapping, and environmental research. The study programme offers courses at a high academic level, providing you with extensive knowledge of applied mathematics and mathematical modelling as well as modern computer equipment and analysis of large data volumes.

You will have the opportunity to work in close collaboration with both Danish and international companies - either as part of the courses, as an individual project or while writing your thesis.

The MSc is a two-year graduate programme with a workload of 120 ECTS credit points.

以上为丹麦技术大学该项目官网项目页原文(英文),本站未做改写或补写。

📋 项目参数

数学建模与计算硕士

Mathematical Modelling and Computation
硕士
语言要求英语(要求 B-level, IELTS, TOEFL, CAE)
📚 课程设置(75 门,理工基础课 7 / 专业课程 10 / 专业核心课 56 / 核心能力课 2)
理工基础课7 门
  • 12100Quantitative methods to assess sustainability (Polytechnical Foundation)
  • 12106Quantitative methods to assess sustainability (Polytechnical Foundation)
  • 12105Quantitative methods to assess sustainability (Polytechnical Foundation)
  • 12101Quantitative methods to assess sustainability (Polytechnical Foundation)
  • 38400Innovation in Engineering (Polytechnical Foundation)
  • 38404Innovation in Engineering (Polytechnical Foundation)
  • 38402Innovation in Engineering (Polytechnical Foundation)
专业课程10 门
  • 38401Facilitating Innovation in Multidisciplinary Teams
  • 38405Facilitating Innovation in Multidisciplinary Teams
  • 38403Facilitating Innovation in Multidisciplinary Teams
  • 02428Dynamic Optimization
  • 02611Optimization for Data Science
  • 02612Constrained Optimization
  • 42114Integer Programming
  • 02409Multivariate Statistics
  • 02443Stochastic Simulation
  • 02582Computational Data Analysis
专业核心课56 门
  • 02492Digital Learning Technology and Entrepreneurship
  • 02810UX Design Prototyping
  • 38102Technology Entrepreneurship
  • 38103X-Tech Entrepreneurship
  • 38106Developing an Entrepreneurial mindset
  • 42576From Analytics to Action
  • 01238Differential Geometry
  • 01257Advanced Modelling - Applied Mathematics
  • 01325Mathematics 4: Analysis - a Toolbox in Physics and Engineering
  • 01405Algebraic Error-Correcting Codes
  • 01415Computational Discrete Mathematics
  • 01425Advanced Cryptography
  • 01426Research Topics in Cryptography
  • 01621Perturbation theory for differential equations
  • 01622Advanced Dynamical Systems: Applications in Science and Engineering
  • 01715Functional Analysis
  • 02231Cryptography Fundamentals
  • 02246Model Checking
  • 02249Computationally Hard Problems
  • 02278Post-Quantum Cryptography
  • 02282Algorithms for Massive Data Sets
  • 02407Stochastic Processes - Probability 2
  • 02411Statistical Design and Analysis of Experiments
  • 02417Time Series Analysis
  • 02421Stochastic Adaptive Control
  • 02425Diffusions and stochastic differential equations
  • 02426Non-linear random effect models: time-independent and dynamic models
  • 02427Advanced Time Series Analysis
  • 02429Analysis of correlated data: Mixed linear models
  • 02431Risk Management
  • 02435Decision-Making Under Uncertainty
  • 02456Deep learning
  • 02458Cognitive Modelling
  • 02460Advanced Machine Learning
  • 02471Machine learning for signal processing
  • 02477Bayesian machine learning
  • 02501Advanced Deep Learning in Computer Vision
  • 02504Computer Vision
  • 02516Introduction to Deep Learning in Computer Vision
  • 02561Computer Graphics
  • 02562Rendering - Introduction
  • 02563Generative Methods for Computer Graphics
  • 02581Geometric Data Analysis and Processing
  • 02614High-Performance Computing
  • 02616Large-scale Modelling
  • 02619Model Predictive Control
  • 02623The Finite Element Method for Partial Differential Equations
  • 02624Inverse problems and Imaging
  • 02686Scientific computing for differential equations
  • 02687Scientific Computing for ordinary and partial differential equations
  • 02689Advanced Numerical Methods for Differential Equations
  • 02807Computational Tools for Data Science
  • 42112Mathematical Programming Modelling
  • 42115Network Optimization
  • 42136Advanced Operations Research Methods
  • 42137Optimization using metaheuristics
核心能力课2 门
  • 01617Introduction to Dynamical Systems
  • 02613Python and High-Performance Computing

📊 申请案例数据分析

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🔗 站内归类方向

该项目已归属站内方向「数学」,可到该校该方向的筛选视图看同方向的其他项目与录取案例

❓ 常见问题

以下问答的答案全部取自本页已列出的官网数据,不做推测;官网未公布的项不出现在这里。
丹麦技术大学数学建模与计算硕士主要学什么?
该项目为硕士,官网介绍:Mathematics is an integrated part of our everyday lives. It is found in mobile phones, train schedules, and online search engines—to give just a few examples.
丹麦技术大学数学建模与计算硕士的语言成绩要求是什么?
英语(要求 B-level, IELTS, TOEFL, CAE)。
丹麦技术大学数学建模与计算硕士一共开设多少门课程?
课程设置共 75 门。
学制 / 学费 / 语言要求 / 申请节奏为按国家(地区)通行区间整理的参考值,各校每年均可能调整,最终以院校官网公布为准;本站不提供中介服务。 排名数据:QS 名次来自 QS 官方榜单整理,U.S. News 名次整理自 Ranking Radar 公开数据集(CC-BY-4.0),原始榜单 © U.S. News & World Report。
数学建模与计算硕士
丹麦技术大学