Ph.D. in Scientific Computing

Examples of Approved Courses for the Ph.D. in Scientific Computing

 

The courses listed below are examples of what past students have taken to fulfill the program’s requirements. This is not an exhaustive list and is subject to change. Approval may also depend on your home department. Please consult with the program coordinator at micde-phd@umich.edu to confirm that any course you plan to take is approved, and for which group. Course plans for the Ph.D. in Scientific Computing are individualized for each student, so please confirm that any given course will work. You can email the program coordinator at micde-phd@umich.edu for more approved courses than are listed here.

If you are preparing a list of proposed courses for the Ph.D. in Scientific Computing audit form, please list all computational coursework you’ve taken or plan to take, whether or not it appears on this list.

If you would like to suggest a computational course for approval, please submit it for review.

Course Plan Review

A course being listed on this page does not constitute approval for your individualized course plan. Please consult with the program coordinator at micde-phd@umich.edu to confirm that any course you plan to take is approved, and for which group.

Methodology

AEROSP 510 Finite Elements in Mechanical and Structural Analysis I
AEROSP 523 / MECHENG 523 Computational Fluid Dynamics I
AEROSP 567 (formerly AEROSP 740) Inference, Estimation & Learning
AEROSP 588 Multidisciplinary Design Optimization
AEROSP 623 Computational Fluid Dynamics II
BIOINF 527 Introduction to Bioinformatics & Computational Biology
BIOSTAT 615 Statistical Computing
BIOSTAT 682 Applied Bayesian Inference
CEE 510 / NAVARCH 512 Finite Element Methods In Solid And Structural Mechanics
CHE 554 / MATSCIE 554 Computational Methods In MATSCIE and CHEM
CHEM 580 Molecular Spectra and Structure
CMPLXSYS 530 Computer Modeling of Complex Systems
DATASCI 500 / STATS 500 Statistical Learning I: Regression (formerly called Applied Statistics I)
DATASCI 503 / STATS 503 Statistics Learning II: Multivariate Analysis
EECS 545 Machine Learning (CSE)
EECS 551 Matrix Methods for Signal Processing, Data Analysis and Machine Learning
EECS 586 Design and Analysis of Algorithms
EECS 592 Artificial Intelligence Foundations
IOE 610 / MATH 660 Linear Programming II
IOE 611 / MATH 663 Nonlinear Programming
MATH 471 Introduction to Numerical Methods
MATH 568 / BIOINF 568 Mathematical and Computational Neuroscience
MATH 571 Numerical Linear Algebra
MATH 572 Numerical Methods for Scientific Computing II
MATH 654 Introduction to Fluid Dynamics
MATH 671 Analysis of Numerical Methods I
MATSCIE 554 / CHE 554 Computational Methods In MATSCIE And CHEM
MECHENG 505 Finite Element Methods In Mechanical Engineering
MECHENG 523 / AEROSP 523 Computational Fluid Dynamics I
NAVARCH 527 / AEROSP 528/ NERS 547 Computational Fluid Dynamics for Industrial Applications
NERS 547 / AEROSP 528 / NAVARCH 527 Computational Fluid Dynamics for Industrial Applications
NERS 561 Nuclear Core Design and Analysis I
PHYSICS 514 Computational Physics
POLSCI 681 Intermediate Game Theory
POLSCI 699 Statistical Methods in Political Research
POLSCI 787 Multivariate Analysis
PSYCH 614 Advanced Statistical Methods
STATS 500 / DATASCI 500 Statistical Learning I: Regression (formerly called Applied Statistics I)
STATS 503 / DATASCI 503 Statistics Learning II: Multivariate Analysis

 

Computational Science/Applications

Must be outside of your home department

AEROSP 510 Finite Elements in Mechanical and Structural Analysis I
AEROSP 523 / MECHENG 523 Computational Fluid Dynamics I
AEROSP 567 (formerly AEROSP 740) Inference, Estimation & Learning
AEROSP 623 Computational Fluid Dynamics II
BIOINF 545 / BIOSTAT 646 / STATS 545 High-throughput Molecular Genomic and Epigenomic Data Analysis
BIOINF 580 Introduction to Signal Processing and Machine Learning in Biomedical Sciences
BIOSTAT 602 Biostatistical Inference
BIOSTAT 615 Statistical Computing
BIOSTAT 646 / BIOINF 545 / STATS 545 High-throughput Molecular Genomic and Epigenomic Data Analysis
CMPLXSYS 530 Computer Modeling of Complex Systems
DATASCI 500 / STATS 500 Statistical Learning I: Regression (formerly called Applied Statistics I)
DATASCI 506 / STATS 506 Computational Methods and Tools in Statistics
DATASCI 507 / STATS 507 Data Science and Analytics using Python
EECS 505 Computational Data Science and Machine Learning
EECS 545 Machine Learning (CSE)
EECS 551 Matrix Methods for Signal Processing, Data Analysis and Machine Learning
EECS 586 Design and Analysis of Algorithms
EECS 587 Parallel Computing
EECS 592 Artificial Intelligence Foundations
MATH 568 / BIOINF 568 Mathematical and Computational Neuroscience
MATH 671 Analysis of Numerical Methods I
MATSCIE 556 Molecular Simulation of Materials
MECHENG 523 / AEROSP 523 Computational Fluid Dynamics I
MECHENG 570 Defects in Materials and Fundamentals of Atomistic Modeling
NAVARCH 527 / AEROSP 528/ NERS 547 Computational Fluid Dynamics for Industrial Applications
NERS 547 / AEROSP 528 / NAVARCH 527 Computational Fluid Dynamics for Industrial Applications
NERS 570 / ENGR 570 Scientific Computing
PHYSICS 514 Computational Physics
SI 650 / EECS 549 Information Retrieval
STATS 500 / DATASCI 500 Statistical Learning I: Regression (formerly called Applied Statistics I)
STATS 506 / DATASCI 506 Computational Methods and Tools in Statistics
STATS 507 / DATASCI 507 Data Science and Analytics using Python
STATS 545 / BIOINF 545 / BIOSTAT 646 High-throughput Molecular Genomic and Epigenomic Data Analysis

 

Mark your calendar for the MICDE SciFM 2024 Conference on April 2nd & 3rd, 2024!

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