Position overview
Salary range:
$70,000.00
Application Window
Open date: November 12, 2024
Most recent review date: Tuesday, Nov 12, 2024 at 11:59pm (Pacific Time)
Applications received after this date will be reviewed by the search committee if the position has not yet been filled.
Final date: Monday, Dec 16, 2024 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
Position description
Per Prof. Dunn: TO WHOM IT MAY CONCERN
I would like to request that Mr. Takuma Kurakami be appointed as a Visiting Assistant Project
Scientist in my laboratory in the Department of Materials Science and Engineering. Mr.
Kurakami is planning to spend one year at UCLA. During this time, he will continue to be paid
by his employer, the Nippon Sheet Glass company, and thus his appointment will be without
salary.
Mr. Kurakami's research during this time will be on the application of machine learning in sol-gel
synthesis of materials. Based on this experimental work, he will be developing algorithms that
can be widely used in materials science.
Qualifications
Basic qualifications
A minimum of a Master's Degree is required.
Application Requirements
Document requirements
Curriculum Vitae - Your most recently updated C.V.
TAKUMA KURAKAMI
Takuma.Kurakami@nsg.com
(072) 781-0081 OBJECTIVE EDUCATION
CONFERENCE PRESENTATIONS EXPERIENCE To conduct quality research with Dunn Lab in University of California Los Angeles while expanding
my knowledge in a new working environment.
Tokyo Institute of Technology
M.S. in Materials Science and Engineering; 2021
The 104th Chemical Society of Japan Annual Meeting; 2024 in preparation The 63rd Symposium on
Glasses and Photonic Materials, PI12; 2022
The 60th Symposium on Glasses and Photonic Materials, 2A10; 2019
Extrapolation Prediction of Properties of Sol-Gel Anti-Reflection Coating with Various Silicon
Alkoxides as Precursors by Machine Learning 2023
NIPPON SHEET GLASS CO., LTD.
Built machine learning models to predict sol-gel coating properties from precursors and synthesis
conditions, and evaluated their extrapolated prediction accuracy by predicting the properties of
the coatings which contain silicon alkoxides as precursors not included in training data. The data
of coating properties for training and evaluation was obtained by measuring optical property and
various durability of the coatings with 8 types of silicon alkoxides as precursors. The use of
molecular descriptors as explanatory variables allows the model to make extrapolated predictions,
and the evaluation results confirmed good prediction accuracy. Reducing Development Time and Number of Experiments for Sol-Gel Anti- Reflection Coatings by
Bayesian Optimization 2023
NIPPON SHEET GLASS CO., LTD.
Developed composition and process of sol-gel anti-reflection coatings by Bayesian optimization, a
typical method of materials informatics. Exploration area and target properties, %Tgain, abrasion
resistance, chemical resistance, and water resistance, were defined that are equivalent to past
development for comparison. By exploring with Bayesian optimization, cycles of experimental data
acquisition and machine learning model building, coating samples with properties that are
equivalent to past composition were obtained in a cycle. As a result, the development time was
reduced from 4 month to 1.5 month and the number of experimental samples were reduced from 82~328
to 40 compared to past development. INTERESTING FACT Development of Temperature-Dependent High Temperature Viscosity Prediction Model of 7-Component
System 2021
NIPPON SHEET GLASS CO., LTD.
Developed a high temperature viscosity model by fitting parameters of MYEGA equation for
7-component oxide melt system.
Black Belt Judo Player (for 13 years). Piano Player (for 12 years).
(Optional)
Cover Letter - TO WHOM IT MAY CONCERN
I would like to request that Mr. Takuma Kurakami be appointed as a Visiting Assistant Project
Scientist in my laboratory in the Department of Materials Science and Engineering. Mr.
Kurakami is planning to spend one year at UCLA. During this time, he will continue to be paid
by his employer, the Nippon Sheet Glass company, and thus his appointment will be without
salary.
Mr. Kurakami's research during this time will be on the application of machine learning in sol-gel
synthesis of materials. Based on this experimental work, he will be developing algorithms that
can be widely used in materials science.
(Optional)
Statement of Research - Extrapolation Prediction of Properties of Sol-Gel Anti-Reflection Coating with Various
Silicon Alkoxides as Precursors by Machine Learning 2023
NIPPON SHEET GLASS CO., LTD.
Built machine learning models to predict sol-gel coating properties from precursors and
synthesis conditions, and evaluated their extrapolated prediction accuracy by predicting the
properties of the coatings which contain silicon alkoxides as precursors not included in
training data. The data of coating properties for training and evaluation was obtained by
measuring optical property and various durability of the coatings with 8 types of silicon
alkoxides as precursors. The use of molecular descriptors as explanatory variables allows the
model to make extrapolated predictions, and the evaluation results confirmed good prediction
accuracy.
Reducing Development Time and Number of Experiments for Sol-Gel Anti-Reflection
Coatings by Bayesian Optimization 2023
NIPPON SHEET GLASS CO., LTD.
Developed composition and process of sol-gel anti-reflection coatings by Bayesian
optimization, a typical method of materials informatics. Exploration area and target
properties, %Tgain, abrasion resistance, chemical resistance, and water resistance, were
defined that are equivalent to past development for comparison. By exploring with Bayesian
optimization, cycles of experimental data acquisition and machine learning model building,
coating samples with properties that are equivalent to past composition were obtained in a
cycle. As a result, the development time was reduced from 4 month to 1.5 month and the
number of experimental samples were reduced from 82~328 to 40 compared to past
development.
(Optional)
Statement of Teaching (Optional)
Statement on Contributions to Equity, Diversity, and Inclusion - An EDI Statement describes a faculty candidate's past, present, and future (planned) contributions to equity, diversity, and inclusion. To learn more about how UCLA thinks about contributions to equity, diversity, and inclusion, please review our Sample Guidance for Candidates and related EDI Statement FAQ document.
Misc / Additional (Optional)
Reference requirements
- 2-4 required (contact information only)
None
Apply link:
https://recruit.apo.ucla.edu/JPF09936 Help contact: mhatanaka@seas.ucla.edu
About UCLA
As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status. For the University of California's Affirmative Action Policy, please visit https://www.ucop.edu/academic-personnel-programs/_files/apm/apm-035.pdf. For the University of California's Anti-Discrimination Policy, please visit https://policy.ucop.edu/doc/1001004/Anti-Discrimination.
Job location
410 Westwood Plaza Los Angeles, CA 90024
|
University of California - Los Angeles (UCLA)
Nov 13, 2024