The group of Professor Gerbrand Ceder at the MIT Materials Science Department has postdoctoral positions available in computational materials design.

The Ceder group is involved in the design of materials, from ab initio computation to experimental synthesis and characterization. Applications include energy capture, conversion and storage. Our experimental efforts in the synthesis and characterization of novel materials work closely with our theory and modeling group, leading to many opportunities for cross-fertilization. More information about our research group can be found at http://ceder.mit.edu. We particularly value innovation and a passion to bridge fundamental scientific inquiry and high-impact applications. Our group offers candidates the opportunity to work in a highly interdisciplinary and dynamic environment. There are no citizenship restrictions. Starting dates are negotiable.

We ask those interested to send their curriculum vitae and references to ceder-hr-comp@mit.edu.


Positions are available in two focused areas:


1. Computational design of materials for novel battery technologies, including Li-ion, Na-ion and Mg-based systems

The ideal candidate will have
- a strong background in solid state physics and thermodynamics,
- good knowledge of ab initio computational methods, such as DFT, DFT+U, GW, TD-DFT, and their application to solids and interfaces,
- experience with molecular dynamics simulations or Monte-Carlo techniques,
- experience modeling optical and/or charge transport processes, and
- some scientific programming skills.


2. Materials informatics within the Materials Project

The Materials Project aims to compute the properties of all known compounds, and make that information available to the materials community to query, learn from and evaluate, thereby creating a Materials Genome. By providing materials researchers with the information they need to design better materials, the Materials Project aims to accelerate innovation in materials research. More information about the Materials Project can be found at http://www.materialsproject.com.

The ideal candidate will have
- a background in thermodynamics and solid state physics,
- experience in scientific software development, for example using Python, Java, C, C++, or Fortran,
- the ability to translate physical models to efficient numerical algorithms, and
- experience with electronic structure calculations based on DFT for crystals and solids.

In addition, we ask the applicant to comment on the Materials Project Crystal Toolkit (http://www.materialsproject.org/apps/crystal_toolkit). In no more than 500 words, tell us how it can be improved in terms of design and functionality.

An application should furthermore include a solution to the following programming assignment.


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Programming Assignment

The Materials Project is almost entirely based on the Python programming language. A combination of materials science and programming skills are an essential part of the job. Write a simple class to represent a composition line, e.g., CoO2-LiCoO2 represents the compositions LixCoO2 where 0 <= x <= 1. Note that the starting and ending compositions can be arbitrary, e.g. Fe2P-MnO is a perfectly valid composition line. For the purposes of this exercise, the compositions can be represented as dicts of {string: float}, e.g., {“Li”:1, “Co”:1, “O”:2}. The class should support at minimum, the following functionality:

(1) A method to determine whether a particular composition lies on a composition line, and the linear coordinates between the two compositions. E.g., Li0.5CoO2 should lie on the CoO2-LiCoO2 line, but not on the LiCoO2-LiMnO2 line.

(2) A method to detect the intersection of two composition lines, if it exists.

(3) Given the energies for the starting and ending compositions, calculate the energy for any point on the line.

You should also provide a set of unit tests that ensures the correct functioning of your class.

Points will be given for code brevity, extensible design, proper documentation, and good use of standard libraries and idioms. You may write your code in Python (preferred) or Java. If using Python, your code should work with Python 2.7 and should not contain any dependencies, with the exception of scientific libraries such as NumPy and SciPy if necessary.