Postdoc Position Openings at MPDC (starting in Spring or Fall
MPDC announces a post doctoral position opening. The position is available
immediately (starting date flexible) with a duration of two years. The successful applicant
must have completed a Ph.D.
in the area of uncertainty quantification (with concentration in mechanics,
materials science, physics or chemistry) and
a proven capacity for world-class research that is reflected in a strong publication record.
Experience in high performance computing is essential with specialization in
at least one of the following areas: Solution of stochastic multiscale PDEs, high-dimensional modeling,
Bayesian inference and machine learning.
Interested candidates should e-mail Prof. N. Zabaras attaching a professional
CV that includes educational background, experience, and links to publications. The application should also
include names, affiliation, telephone numbers,
and e-mail addresses of three individuals who could provide letters of reference, if needed.
Only applications with relevant
background will receive full consideration and response.
Graduate Research Assistant (GRA) Position
Openings (PhD program only) at MPDC
Several graduate research assistantships (GRAs)
are available at MPDC for the academic year 2011-2012. All GRA positions are in the broad
area of scientific computing and in particular target
stochastic/statistical multiscale/multiphysics modeling and design of
materials. We are looking for
mature, professional and intelligent applicants from around the world who are highly
self-motivated in pursuing a Ph.D. in
the interface of computational mathematics and materials (please read carefully
this document discussing the
expected qualifications and academic credentials for successful applicants to our program,
type of work conducted in our laboratory, and information about preparing your application). Complete information
for applying to the Mechanical Engineering (ME)
or the Aerospace Engineering (AE)
or the Applied Mathematics (CAM) fields is available on
the Cornell graduate school web site.
Deadline for applying January 1, 2012 (ME and AE fields) and January 15, 2012 (CAM).
Three recommendation letters,
GRE General Test (GRE subject test in mathematics advised for
applicants to the Appl. Math. field) and
a resume indicating research experience and academic ranking,
a research statement of purpose clearly addressing qualifications and interests for the
open positions at MPDC, research publications (if any) and a non-refundable application fee
are required for applying to Cornell's graduate program.
Minimum TOEFL score requirements: reading (20),
writing (20), listening (15), and speaking (22).
For any additional information in applying to Mech. or Aero fields, please contact
Marcia Sawyer who administers these fields. For information
regarding applications to the Center for Applied Mathematics, contact us at this address.
Applicants are required to document their background, interests and competence to work in computationally intensive
projects. They should have a demonstrated knowledge of C++, excellent undergraduate record in the broad
area of applied mathematics and some exposure to probability and statistics.
Applicants are required to have a B.S. degree in any area of engineering, applied mathematics or physics.
Applicants with an M.S. degree and/or prior
documented research experience in multiscale materials modeling are
particularly encouraged to apply. We are
aggressively recruiting qualified female and under-represented minority students.
Please note that due to time constraints
applications that do not address directly their relevance to the advertised positions will not receive any response.
Those interested in these positions are
apply online directly to the Cornell Graduate School as indicated above. For
applicants to the ME or AE fields,
select the area of `Engineering Materials'. For those applying to CAM, indicate in your
statement of purpose your interest to work with us in computational sciences.
Once your electronic submission is complete and in
order to accelerate the review process,
we recommend that you notify us via Email at this address.
Curriculum for Doctoral Studies
The main focus of our work is on the multiscale stochastic modeling
and design of materials. All graduate students participating in this research are
expected that, in addition to specializing in particular area(s) in the mechanics and physics of
materials, will also acquire a strong
background (effectively a minor) in mathematics, stochastic/statistical modeling and/or computational sciences.
The following includes a list of suggested readings for graduate
students joining MPDC to identify potential graduate courses of interest. A key 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 successful researcher.
Suggested readings in Mechanics and Physics of Materials:
- An introduction to continuum Mechanics, by M.E. Gurtin
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
structure of materials, by S.M. Allen, E.L. Thomas
- Introduction to thermodynamics of materials, by D.R. Gaskell
transformations in metals and alloys, by K.E. Easterling & D.A. Porter
of materials, by R.W. Balluffi, S. M. Allen, and W.C. Carter
- Fundamentals of
solidification, by W. Kurz & D. J. Fisher
- Principles of Quantum
Mechanics, by R. Shankar
- Introduction to solid state physics,
by C. Kittel
structure of materials , by A.P. Sutton
structure: Basic theory and practical methods,
by R. M. Martin
to computational chemistry,
by F. Jensen
- Molecular modeling: Principles and applications,
by A. Leach
Suggested readings in Computational Mathematics, Statistics and Stochastic Modeling:
- Matrix computations, by G.H. Golub & C.van Loan
- Numerical linear algebra, by L.N. Trefethen & D. Bau
Finite element method: Linear static and dynamic finite element analysis , by T.J.R. Hughes
solution of partial differential equations by the finite element method, by C. Johnson
- Optimization by vector space methods, by D.G. Luenberger
optimization, by J. Nocedal & S. Wright
- Numerical Analysis, by
R. L. Burden and J. D. Faires
Scientific Computing in C++ and MPI,
by G. Karniadakis, R.M. Kirby
- Code Complete: A Practical Handbook of Software Construction,
by S. McConnel
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
- Monte Carlo
Statistical Methods, by CP. Robert.
- The elements
of statistical learning, by T. Hastie, R. Tibshirani & J.H. Friedman
Solution of Stochastic Differential Equations, by P. E. Kloeden and E. Platen
- Stochastic Finite Elements: A Spectral Approach, by R.G. Ghanem & P.D. Spanos
Suggested readings in Applied Mathematics and Stochastic Analysis: