ר"צ חשמל לאתר באזור השפלה - 8197

  • מהנדס/ת חשמל בעל רישיון חשמלאי מהנדס – חובה!
  • ניסיון של 10 שנים לפחות בניהול ובביצוע אחזקה באתרים בעלי שטח של 20000 מ"ר לפחות שכללו מתקני חשמל ומתקני מיזוג אוויר מרכזי בתפוקה של 1000 טון קירור לפחות וגודל מתקן מתח גבוה של 5000KVA.
  • הגעה לאתר באירועי תקלה מיוחדים, השבתות ועבודות יזומות לפי הצורך.
  • ידע בתחום מערכות החשמל, באחזקת מערכות מיזוג אוויר ואינסטלציה.
  • שליטה בשפות:
  • עברית – ידיעה מלאה.
  • אנגלית – קריאה דיבור והבנה של חומר מקצועי.
« חזרה לכל המשרות

Junior DFT Engineer

BASIC QUALIFICATIONS

· Excellent BSC/MSC Electrical/Computer engineering
· Team player , Highly motivated and willing to work in dynamic environment
· Willing to work in full time position

 

PREFERRED QUALIFICATIONS

· Chip design, Verilog and System Verilog

« חזרה לכל המשרות

Junior Chip Design Engineer

BASIC QUALIFICATIONS

  • Excellent BSC/MSC Electrical/Computer engineering
  • Team player , Highly motivated and willing to work in dynamic environment
  • Willing to work in full time position

 

PREFERRED QUALIFICATIONS

  • Chip design, Verilog and System Verilog
  • Personal characteristics: Team player, highly motivated, willing to work in dynamic and demanding environment and Fast learner
« חזרה לכל המשרות

Chip Design Student

BASIC QUALIFICATIONS

  • Student in Computer Engineering/BS Computer science/Electrical Engineering
  • GPA 88 and above (need to send grade sheet)

 

PREFERRED QUALIFICATIONS

  • Fluent English
  • Available for 2-3 work days per week
« חזרה לכל המשרות

Software Student

BASIC QUALIFICATIONS

· Computer Science/ Computer Engineering Student
· GPA 85 and above (need to send grade sheet)
· Fluent English
· Available for 2-3 work days per week
 

PREFERRED QUALIFICATIONS

· Experience with python development

« חזרה לכל המשרות

Applied Scientist Student

BASIC QUALIFICATIONS

· Student (BSc/MSc/PhD) in Computer Science or Electrical Engineering.
· First related work experience in software development (design, implementation and testing).
· Knowledge in programming languages such as Python and C/C++.
 

PREFERRED QUALIFICATIONS

· Experience in Machine Learning or Computer Vision.
· Experience in web development (AngularJS, Flask etc.)
· Fast learning and team player

 

« חזרה לכל המשרות

Devicr R&D Intern -3283

  • Student in the 2nd or 3rd year of BSc in Physics / Computer Science / Materials Engineering Electrical (Microelectronics  - advantage) – at  least 2 years of engagement /
  • Or Student in the beginning of the second degree in the same fields
  • Hands on, independent, good English
  • Strong background in programming / scripting - emphasis on Matlab, C  and Python
  • Background in semiconductors, solid state device physics, Flash memory – advantage
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סמינר מחלקתי בזום - אלירן רפאל חמו, סטודנט תואר שלישי

10 בנובמבר 2020, 15:00 - 15:40 
ZOOM  
ללא תשלום
סמינר מחלקתי בזום - אלירן רפאל חמו, סטודנט תואר שלישי

סמינר מחלקתי בזום 
PHD Department Seminar
Time: Tuesday, November 10th, 15:00

:You are cordially invited to attend this seminar to be held on 10.11.2020 at 15:00

Stable and Active Catalyst supports Designed based on Transition Metal Carbides for Hydrogen Fuel Cells

Eliran Rephael Hamo, Ph.D. Student

Under the supervision of Dr. Brian Rosen

Polymer electrolyte membrane fuel cells (PEMFCs) are one of the most promising products of the 21st century for power. Their use encompasses portable applications, transportation, and a stationary grid-power mainly due to their low-temperature operation and quick start-up. However, the primary challenge is improving fuel cell durability to meet updated U.S. Department of Energy targets (e.g. 8000+ hours for portable applications). PEM fuel cell catalysts currently suffer from low durability, undermining their wide-scale deployment into the consumer and industrial markets. Platinum is still the most common metal used in PEMFCs as it provides among the highest activity for electrode reactions and lifetime stability. An effective way to decrease Pt loading is the adoption of supports to enhance both Pt dispersion and its utilization. Requirements for such support include factors such as surface area, conductivity, and electrochemical and mechanical stability. Carbon is currently the industrial standard for supporting the Pt catalyst particles, yet carbon-supported catalysts suffer from low durability. Corrosion of the carbon-based support was identified to be the major contributor to performance degradation as they suffer from corrosion via carbon oxidation to CO2 (at the cathode). This phenomenon exacerbates related issues such as Pt sintering or agglomeration. Therefore, there is a significant interest in exploring stable alternatives to replace carbon supports in PEM fuel cells.

Transition metal carbides (TMCs) have attracted significant attention over the last several years as a possible replacement for carbon-based catalyst supports in fuel cells. TMCs exhibit electronic structures similar to Pt-group metals and have been shown to enhance the catalytic activity of fuel cell reactions in part to their strong metal-support interaction (MSI). Despite these advantages over carbon supports, the large-scale deployment of TMC-based supports in fuel cells is still hindered by concerns of durability at the high potential on the cathode during start-up and shutdown operation. Molybdenum carbide in particular has been the center of attention as it imbues high activity for oxygen reduction, yet unprotected Mo2C will begin to oxidize just over 0.4V vs. RHE making them less practical for use as cathode catalysts support.

Here, we modify both the bulk and surface of Pt/Mo2C catalysts and apply them to room-temperature fuel cells which operate under both acidic (as cathode) and alkaline (as anode) environments.  The co-reduction carburization method enabled the low-temperature preparation of TMC alloy supports (e.g. Mo2C-TaC, Mo2C-W2C). By contrast, DC magnetron sputtering was used to modify the surface of the carbide catalysts with Ta-based phases. Bulk alloy formation such as Mo2C-TaC showed enhanced corrosion resistance in acidic fuel cells, yet this came at the expense of activity. By contrast, when the same bulk Mo2C-TaC alloys were employed in alkaline fuel cells (at the anode), increased durability was observed together with increased activity. Experimental and computational efforts by us have shown that durability was attributed to the oxygen binding energy (OBE) of the carbide while activity was attributed to enhanced metal-support interaction, which varied as a function of carbide composition. Despite the fact that bulk alloying with TaC diminished the performance of Pt/Mo2C in acidic fuel cells, the addition of a protective Ta layer to Pt/Mo2C by magnetron sputtering was shown to increase both activity and durability.  Engineering of the support (rather than the metal catalyst) by bulk and surface techniques should therefore be considered as a strategy to simultaneously improve activity and durability in energy conversion and storage systems.

 

~~
Topic: PHD Department Seminar
Time: Tuesday, November 10th, 15:00
https://us02web.zoom.us/j/85326142774?pwd=ZVRPODI3RTYyc2JFSmNZZi9mbzZPQT09

 

EE Seminar: Generalization in Overparameterized Machine Learning

23 בנובמבר 2020, 15:00 
ZOOM  

Zoom link: https://us02web.zoom.us/j/83932011090?pwd=WjlxK2hOczFvUkQxNy9yQXFLVzJaUT09
Meeting ID: 839 3201 1090
Passcode: TAUEESYS

Speaker: Dr.  Yehuda Dar

Electrical and Computer Engineering Department at Rice University

Monday, November 23rd, 2020, at 15:00

Generalization in Overparameterized Machine Learning

Abstract

            Modern machine learning models are highly overparameterized (i.e., they are very complex with many more parameters than the number of training data examples), and yet they often generalize extremely well to inputs outside of the training set. This practical generalization performance motivates numerous foundational research questions that fall outside the scope of conventional machine learning concepts, such as the bias-variance tradeoff.

This talk presents new analyses of the fundamental factors that affect generalization in machine learning of overparameterized models. We focus on generalization errors that follow a double descent shape with respect to the number of parameters in the learned model. In the double descent shape, the generalization error arrives at its peak when the learned model starts to perfectly fit the training data; but then the error begins to decrease again in the overparameterized regime. Moreover, the global minimum of the generalization error can be achieved by a highly complex (overparameterized) model even without explicit regularization. The first part of the talk considers a transfer learning process between source and target linear regression problems that are related and overparameterized. Our statistical analysis demonstrates that the generalization error of the target task has a two-dimensional double descent shape that is significantly influenced by the transfer learning aspects. Our theory also characterizes the cases where transfer of parameters is beneficial. The second part of the talk introduces a new family of linear subspace learning problems that connect the subspace fitting (using principal component analysis) and regression approaches to the problem. We establish a numerical optimization framework that demonstrates the effects of supervision level and structural constraints on the double descent shape of the generalization error curve.

 

Short Bio

Yehuda Dar is a postdoctoral research associate in the Electrical and Computer Engineering Department at Rice University, working with Prof. Richard Baraniuk on topics in the theory of modern machine learning. Before that he was a postdoctoral fellow in the Computer Science Department of the Technion — Israel Institute of Technology, where he also received his PhD in 2018. Yehuda earned his MSc in Electrical Engineering and a BSc in Computer Engineering, both also from the Technion. His main research interests are in the fields of machine learning theory, signal and image processing, optimization, and data compression.

 

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז.  בצ'אט

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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