Position Openings for 2008-2009
Several graduate research assistantships (GRAs)
are available at MPDC for the academic year 2008-2009. We are looking for
well-qualified applicants from around the world who are highly-motivated in pursuing their Ph.D. in
the interface of computational mathematics and materials.
We will consider applications for admission
in the Spring 2009 (deadline for applying November 1, 2008) and/or the Fall 2009 (deadline
for applying January 1, 2009). Complete information for applying to the Mechanical Engineering (ME)
or the Aerospace Engineering (AE) fields is available on
the Cornell graduate school web site. Three recommendation letters,
academic transcripts,
GRE and TOEFL scores, a resume indicating research experience and academic ranking, a research statement of purpose, research publications (if any) and a non-refundable application fee
are required for applying to Cornell's graduate program. Minimum GRE/TOEFL scores and other information
is described here. For any additional information, please consult
Marcia Sawyer who administers the ME and AE fields.
The ideal applicant has a strong
background in mathematics and computational sciences, substantial programming skills (C++),
strong background and interests in materials/mechanics, and prior exposure to probability/statistics.
Applicants are required to have a B.S. degree in any area of engineering
or in physical sciences (e.g. applied mathematics, physics, or chemistry). Applicants with an M.S. degree and/or prior graduate
research experience are
particularly encouraged to apply. We are
aggressively recruiting female and under-represented minority students. Two research
areas are receiving an increasing attention
in our laboratory: Stochastic multiscale modeling/design of materials & processes and electronic structure calculations of
solids with emphasis on materials-by-design.
Those interested in these positions are
required to
apply online directly to the Cornell Graduate School (ME or AE fields) as indicated above.
Indicate explicitly in your
application your interest to work in our laboratory and your related qualifications. Once your electronic submission is complete and in
order to accelerate the review process,
we recommend that copies of the material that you downloaded in the Graduate School web site is also Emailed to us. If you are interested in these positions but require additional
information before you formally apply, feel free to contact with any inquiries Prof. N. Zabaras at this Email address.
Suggested
Curriculum for Doctoral Studies
The main focus of our work is on the development of computational algorithms for the design and control of materials and materials processes. All graduate students participating in this research are expected that, in addition to specializing in particular area(s) of applied sciences (e.g. mechanics of materials, materials science, thermal/fluid sciences), will also aquire a strong background in computational mathematics, computational Bayesian statistics, finite element analysis, optimization/control theory, functional analysis, and/or inverse problems.
The following includes a list of suggested readings for new graduate
students joining MPDC. This list is
compiled from the courses taken by recent graduate students in our
laboratory. Graduate students that
join our laboratory are suggested to take 3 courses per semester
in their designated research areas to the level of the textbooks given below. This textbook list is
provided here only to show the type of background MPDC students are expected to acquire. For clearer
demonstration of the type of work we perform at MPDC please visit our publications web site.
A major distinctive feature of
our program is the requirement for all students to participate to some level of research
activity immediately upon joining MPDC. This approach helps students to fine tune
their courses to research objectives but mainly allows them for an early development of an
independent & creative thinking needed
for a succesfull researcher.
Suggested readings in Mechanics of Materials:
- An introduction to continuum Mechanics, by M.E. Gurtin
- Classical and computational solid mechanics, by Y. C. Fung & P. Tong
- Fracture mechanics, fundamentals and applications , by T.L. Anderson
- Continuum mechanics and plasticity, by H.-C. Wu
- Computational inelasticity, by J.C. Simo & T.J.R. Hughes
Suggested readings in Materials Sciences:
- The structure of materials, by S.M. Allen, E.L. Thomas
- Introduction to thermodynamics of materials, by D.R. Gaskell
- Phase transformations in metals and alloys, by K.E. Easterling & D.A. Porter
- Kinetics of materials, by R.W. Balluffi, S. M. Allen, and W.C. Carter
- Fundamentals of solidification, by W. Kurz & D. J. Fisher
Suggested readings in Atomistic Modeling of Materials:
- Principles of Quantum Mechanics, by R. Shankar
- Introduction to solid state physics, by C. Kittel
- Electronic structure of materials , by A.P. Sutton
- Electronic structure: Basic theory and practical methods, by R. M. Martin
- Introduction to computational chemistry, by F. Jensen
- Molecular modeling: Principles and applications, by A. Leach
Suggested readings in Thermal and Flow Sciences:
- Principles of heat transfer, by M. Kaviany
- Incompressible flow, by R.L. Panton
- Turbulent flows, by S. Pope
Suggested readings in Computational mathematics:
- Matrix computations, by G.H. Golub & C.van Loan
- Numerical linear algebra, by L.N. Trefethen & D. Bau
- The Finite element method: Linear static and dynamic finite element analysis , by T.J.R. Hughes
- Numerical solution of partial differential equations by the finite element method, by C. Johnson
- Parallel Scientific Computing in C++ and MPI, by G. Karniadakis, R.M. Kirby
Suggested readings in Computational statistics:
- Statistical inference, by G. Casella & R.L. Berger
- Monte Carlo strategies in scientific computing, by J. S. Liu
- Elements of computational statistics, by J. E. Gentle
- Bayesian statistics, by P. Lee
- Marlov chain Monte Carlo in practice, by W.R. Gilks
- The elements of statistical learning, by T. Hastie, R. Tibshirani & J.H. Friedman
Suggested readings in Stochastic Modeling:
- Introduction to probability models, by S.M. Ross
- Introduction to stochastic processes, by G.F. Lawler
- Stochastic finite elements, by R.G. Ghanem & P.D. Spanos
Suggested readings in Optimization and Control Theory:
- Optimization by vector space methods, by D.G. Luenberger
- Numerical optimization, by J. Nocedal & S. Wright
- Introduction to stochastic search and optimization, by J.C. Spall
- A course in robust control theory, by G.E. Dullerud & F. Paganini
