[BNC-all] BNC E-news: Week of July 12th, 2021

Mon Jul 12 13:42:29 EDT 2021

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Seminars /Workshops / Events

nanoHUB is excited to announce the next workshop in the Summer 2021 session of our Hands-on Data Science and Machine Learning Training Series.
Series information. Our series is aimed at active researchers and educators and designed to introduce practical skills with online, hands-on activities that participants will be able to incorporate in their own work. Hands-on activities will use nanoHUB cloud computing resources, negating the need to download or install any software. All that is required of the audience is an internet connection and an hour to spare for the demonstration. After the training sessions, you will be able to continue using nanoHUB for research or education.
Registration links and material for prior workshops can be found at the workshop webpage: https://nanohub.org/groups/ml/handsontraining
Please register soon as seats are limited. Share with any colleagues who may be interested.
Title: A Machine Learning aided hierarchical screening strategy for materials discovery
Date: July 21st, 2021, 1:30 PM - 2:30 PM EST
Speaker: Anjana Talapatra, Postdoctoral Fellow, Los Alamos National Laboratory
Register here (limited seats): https://nanohub.org/groups/ml/handsontraining
Abstract: One of the most basic approaches to problem solving is to conceptualize the problem at different abstraction levels and translate from one abstraction level to the others easily, i.e., deal with them hierarchically. This concept is especially applicable to the field of novel materials discovery, wherein large candidate domains can be quickly and efficiently explored by hierarchically discarding irrelevant candidates. In this tutorial, we illustrate this approach using the example of wide band gap oxide perovskites. We will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator nature and have a wide band gap. To this end, we will build four machine learning (ML) models: three classification and one regression model using experimental and DFT-calculated training data. The tutorial will discuss best practices for building ML models, commonly encountered pitfalls and how best to avoid them.

BNC Faculty Seminar Series (will resume fall semester, 2021)

Previous Talks: https://engineering.purdue.edu/Intranet/Groups/BNC/FacultySeminars

Birck Events<https://www.purdue.edu/discoverypark/birck/events/index.php> and Birck News<https://www.purdue.edu/discoverypark/birck/news/index.php>

Birck Nanotechnology Center Advanced Capabilities

[cid:image002.jpg at 01D77723.BDB93030]Keyence 950F Digital Microscope<https://wiki.itap.purdue.edu/display/BNCWiki/Keyence+VHX-950F+Digital+Microscope>

  *   Two lenses with magnification from 20x to 2000x
  *   Anti-vibration control

  *   Multiple image viewing options and 3D viewing and measuring

Location: Cleanroom N Bay
Contact: Bill Rowe (wrowe at purdue.edu<mailto:wrowe at purdue.edu>)
Please visit the Birck Wiki<https://wiki.itap.purdue.edu/display/BNCWiki/> to learn about the wide array of fabrication and characterization equipment at the facility

New Engineering Staff Member

Please welcome Dan Witter, our newest member of the Birck Engineering Staff!  Dan comes to us with a Master's Degree in Chemical Engineering from the University of Colorado, Boulder.  He has worked for Raytheon Space and Airborne Systems, he had a NASA Internship at Ames Research Center, and he worked in Quality Assurance for W. L. Gore and Associates.  Additionally he served as a Metallurgical Intern at Climax Molybdenum Company, as a Lab Safety Manager for Chem-E-Car at the University of Arizona, and as a radio DJ.  Dan will be working as a deposition engineer in the Scifres cleanroom.  Please say hello and welcome Dan to the Birck team.

Ron Reger

Post Doc Position

Postdoctoral positions are available at the optical super-resolution microscopy lab, directed by Dr. Fang Huang at Purdue University, Biomedical Engineering Department. The candidates are expected to spearhead innovations related to one of the two areas of research focus:
1. Fluorescence nanoscopy through tissue specimens. We develop novel imaging instruments and analytical methods to visualize intra- and extra-cellular structures at the nanoscale in thick specimens such as tissues or small animals. In the past, the team developed novel Adaptive Optics and PSF engineering methods to super-resolve brain sections (Nature Methods, 15, 583-586, 2018), deep learning framework for multiplexed single molecule analysis (Nature Methods, 15, 913-916, 2018) and INSPR (in situ PSF retrieval) technology to expand applicability of super-resolution imaging system from cells to tissues (Nature Methods, 17, 531-540, 2020).
2. Molecular resolution imaging of live cells. We seek to develop unconventional ultra-high resolution systems that synergistically combines ideas from engineering and physics such as interferometric/4Pi single molecule detection (Cell, 166, 4, 1028-1040, 2016), applied statistics (Nature Methods, 10, 653-658, 2013; Nature Methods, 14, 760-761, 2017) and coherent pupil function (Communications Biology, 3, 220, 2020) to significantly advance the achievable resolution limit for fixed and living specimens.
The Huang lab also collaborate extensively with cell biologists, neuroscientists, and chemists to tackle fundamental biological questions in areas such as cytokinesis, epigenetics, neural circuits, and cell motility. We apply our imaging technologies to reveal disease mechanisms in Alzheimer's disease, autism, and cancer. A list of the group's recent work can be found here: https://www.fanghuanglab.com/publications.html. The lab's research is mainly funded through awards from NIH, DARPA and BRAIN Initiative.
The applicant will have a PhD related to one (or more) of the areas in optics, microscopy, applied statistics or signal/image processing. Experiences in optical design and instrumentation, microscopy data analysis, or optical theory will be helpful.
Inquiries and applications (including CV, name/email address of 2-3 referees, and reprints of 2 most significant publications) should be directed to:

Fang Huang, Ph.D.

Assistant Professor, Weldon School of Biomedical Engineering, Purdue University

fanghuang at purdue.edu<mailto:fanghuang at purdue.edu>

Web: https://www.fanghuanglab.com<https://www.fanghuanglab.com/>

Applications will be stay open until filled.

Fall Grad Course

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Jaime Turner
Lead Administrative Assistant to the Director | Birck Nanotechnology Center
BRK | 1205 W State Street | West Lafayette, IN 47907
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