The group of Professor Gerbrand Ceder at the Department of Materials Science and Engineering at UC Berkeley and Lawrence Berkeley National Laboratory has postdoctoral positions available.
The Ceder group is involved in the development of materials and synthesis theory and design of materials, from ab initio computation to experimental synthesis and characterization to machine learning. Applications include energy capture, conversion, and storage. Our theory and modeling work is done in close collaboration with our experimental group for synthesis and characterization of novel materials, leading to many opportunities for cross-fertilization. We give individuals the opportunity to collaborate on multiple internal and external projects, as well as supervise graduate students. Many of our alumni have gone on to leading positions in academia and in the private sector. More information about our research group can be found at http://ceder.berkeley.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 firstname.lastname@example.org.
Currently, the Ceder group has positions available in computational design and understanding of energy materials, experimental synthesis and characterization of energy materials, and machine learning and natural language processing for CovidScholar.
1. Studying of Materials Synthesis with NLP and Machine Learning Techniques
The successful candidate will employ and extend Machine Learning and Natural Language Processing techniques to train computer models for understand materials synthesis papers, and apply these models to discover, deliver and verify new insightful knowledge on materials synthesis theory. The ultimate goal of this project is to construct a comprehensive theory that may predict and guide experimental designs in materials synthesis. The position requires:
- Basic level of understanding in machine learning,
- Basic understanding of thermodynamics, phase diagrams, common materials synthesis procedures, etc.,
- Adequate experience in computer programming and software development, preferably in Python, and
- Team working skills.
Experience in the following areas is a plus:
- Background inphysics, chemistry related fields,
- Knowledge in Natural Language Processing,
- Database administration,
- Basic knowledge in website technologies (HTMLs etc.),
- Github, and
- Familiarity with one deep learning software framework (tensorflow, keras, pytorch).
2. Scientific Data and Text Mining with Machine Learning Techniques
The successful candidate will employ and extend existing data mining and machine learning tools to identify materials with desired properties and will develop novel machine-learning approaches for the discovery of composition-structure-property relationships. This project will involve generating and handling large data sets, such as those in the Materials Project (https://www.materialsproject.org), or other internally generated datasets. The position requires:
- Excellent scientific development skills, preferably in the Python programming language,
- Good understanding of thermodynamics and phase diagrams, and
- Some experience with machine-learning and data-mining/text-mining techniques.
Experience in atomistic simulations, preferably based on density-functional theory, is a plus.
3. Computational Understanding and Discovery of Novel Battery Materials
The successful candidate will work in close collaboration with experimental colleagues by predicting novel materials, providing synthesis guidelines, and/or understanding experimental observations. The position requires:
- A strong background in solid state physics,
- Excellent practical knowledge of density-functional theory, and
- Good knowledge of thermodynamics and statistical mechanics.
Scientific programming skills and experience with electrochemical energy storage are a plus.
4. Theory and Modeling to Predict Materials Properties, Phase stability, and Synthesis
The candidate will work on the development of novel methods for the prediction of materials properties and phase stability. We are particularly interested in the prediction of phase stability and metastability. The position requires:
- Excellent knowledge of thermodynamics, statistical mechanics, and kinetics of materials,
- Good practical knowledge of density-functional theory, and
- Working knowledge of crystallography.
Experience in the modeling of surfaces and interfaces is a plus.
Experimental postdoctoral positions in the group of Professor Gerbrand Ceder at UC Berkeley and the Lawrence Berkeley National Laboratory
The group of Professor Gerbrand Ceder at the UC Berkeley Materials Science Department and Lawrence Berkeley National Laboratory has two postdoctoral positions available in experimental investigation and design of materials for energy storage and conversion. The Ceder group is involved in the design of technologically-important materials, from ab initio computation to experimental synthesis and characterization. Applications include energy capture, conversion and storage, e.g. photovoltaics, thermoelectric, lithium-ion and sodium-ion batteries. Our experimental efforts in the synthesis and characterization of novel materials are guided, as well as informed, by our computational work, leading to many opportunities for cross-fertilization. More information about our research group can be found at http://ceder.berkeley.edu.
1. Position #1: The successful candidates will have experience in some of the following areas:
- Synthesis of bulk or nanoscale solid state inorganic materials (e.g. oxides, phosphates),
- Electrochemical, spectroscopic, or microscopic methods of materials characterization, including for:
a. understanding relationships between structure and ionic/electronic properties
b. discerning property changes between bulk and nanoscale, and
- Candidates with experience in Li- or Na-ion batteries, powder X-ray crystallography are particularly encouraged to apply.
2. Position #2 : The successful candidates will have experience in some of the following areas:
- Organic/conductive polymer/ionic liquid synthesis and electrochemical characterization methods,
- Electrochemical energy storage systems, including but not limited to: batteries, supercapacitors, and
- We particularly value innovation and a passion to bridge fundamental scientific inquiry and high-impact applications.
A successful candidate must have a PhD in materials science, physics, chemistry, chemical engineering, or a related field by the commencement of employment. There are no citizenship requirements. Starting dates are negotiable. We ask those interested to send their curriculum vitae and references to email@example.com.
Postdoctoral Positions in Machine Learning and Natural Language Processing for CovidScholar
The COVIDScholar project, led by Professor Gerbrand Ceder at the Lawrence Berkeley National Laboratory, currently has a postdoctoral position available. COVIDScholar is an effort to aggregate, disseminate, and analyze research literature relating to COVID-19.
A successful candidate will employ machine learning and natural language processing techniques to develop models and produce insights from our cross-disciplinary corpus of scientific papers for applications such as alignment of unstructured textual knowledge to ontologies, drug repurposing, and literature search.
Due to the highly multidisciplinary nature of this project, this role will involve collaborating closely with domain experts to understand their needs and will have considerable scope for the successful candidate to define projects in alignment with their interests and experience. A candidate with relevant experience would also have the opportunity to collaborate on text-mining projects within the group focused on materials science.
This position requires:
- Understanding of natural language processing techniques and machine learning,
- Experience in software development, preferably in Python,
- Basic understanding of web development, and
- Familiarity with scientific literature and the academic publication process.
The following are not required but will be considered a plus;
- Background in biomedical sciences,
- Experience with knowledge graphs and ontologies, and
- Previous work contributing to software infrastructure and data pipeline.
Applicants should send a CV, cover letter, and the names of 3 potential letter writers to firstname.lastname@example.org