Material Planner

  • B.S.C in industrial engineering from a leading and recognized university
  • 3 years of experience in operational & planning roles
  • Ability to gain the cooperation of others, cooperate and lead complex interfaces with no authority
  • Ability to work under pressure, manage and prioritize multiple tasks in parallel and manage timelines & due dates independently
  • Very good analytical skills, sharp & fast thinker
  • High interpersonal skills, ability to lead and cooperate with complex interfaces
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Deep Learning Intern (3 months) for Ph.D, M.Sc student

 

What are we looking for?

  • PhD/MSc student in EE/CS/Mathematics, in the field of Deep Learning
  • 2+ years of experience in Deep Learning or Computer Vision
  • Proficient in python and in DL frameworks (TensorFlow or Pytorch)
  • Problem solving, innovative approach

 Advantages

  • Previous publications in relevant fields
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סמינר מקוון עם אמיתי קליגמן

 Amitay Kligman

M.Sc. student under the supervision of Dr. Yuval Beck

 

 

18 במרץ 2021, 17:00 
זום  
סמינר מקוון עם אמיתי קליגמן

קישור לזום

אבסטרקט

 

 

Non-Intrusive Load Monitoring (NILM) process is intended for separating individual power features from an aggregated energy reading, in order to estimate the operation of individual appliances.

In the past, electricity meters specified only active power reading, for billing purposes, limiting the abilities of NILM solutions. However, recent progress in smart metering technology introduced cost-effective, household consumer-grade metering products, which can produce multiple features with high accuracy.

In this research, a new technique is proposed to apply BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) method as part of a multi-dimensional load disaggregation solution, based on extraction of multiple features from a smart meter.

The new technique is called Disaggregation in NILM by means of multidimensional BIRCH clustering (DNB).

The main contribution of this research is a technique, comprises four-steps for NILM solution that utilizes a multi-dimensional feature-space with adaptation to power quality parameters.

The proposed algorithm is simple, fast, light-weight, can use raw data samples from a smart meter, can use low-frequency samples reading and does not require a preliminary training or learning process. The proposed method was tested using a private dataset, demonstrating a good use of multi-dimensional space. Further tests involved a limited-dimensional scenario, using AMPds public dataset, in order to compare performance to other NILM algorithms. Some additional experiments in a severe-noise environment were also performed, in order to further explore DNB boundaries and limitations.

 

 

 

 
 
 
 

סמינר מקוון עם סיון ניב

 

Sivan Niv

M.Sc. student under the supervision of Dr. Amir Natan

28 בפברואר 2021, 14:00 
זום  
סמינר מקוון עם סיון ניב

קישור לזום

אבסטרקט

 

The evaluation of atomic forces and total energy is a key challenge for large-scale atomistic simulations of materials. In recent years, machine learning techniques are successfully used to predict potential energies and derive the atomic forces through their gradient. In such methods, the training data is produced by quantum calculations, typically Density Functional Theory (DFT).

The direct prediction of atomic forces by deep learning (DL) models was demonstrated by other groups and us.

It has the advantage of being local and slightly faster while still maintaining state of the art mean absolute error (MAE). A disadvantage is that the forces might be non-conserving.

Like models that predict the energy, direct force models should behave well under symmetry operations and permutation of atoms.

Here, we show how the use of self-learned embedding layers and data augmentation help to achieve both goals. We evaluate the model's reliability by several measures: the resulting mean average error (MAE), the calculation of phonons spectra in several solids, the behavior of force derivatives under atom movement, and finally, the resulting radial distribution function (RDF) after molecular dynamics runs which use the model forces at several temperature values.

We show that the MAE alone is not a sufficient measure for a successful model.

 

 

 
 
 
 

מתוך חדשות הבוקר קשת  21.2.21

Ynet 19.2.21

כאן 20.2.21

​בחדרי חרדים  20.2.21

גלי צה"ל  21.2.21

ערוץ18.2.21 20il   

ישראל היום  20.2.21

פורטל הכרמל 20.2.21

עיתון מעריב  21.2.21

Isreal Defnce 21.2.21

ערוץ7  18.2.21

כיכר השבת  20.2.21

מהנדס/ת מעבדת הוראה במבנה המחשב

ידע נדרש:

  •  הכרת סביבה עבודה לתכנון חומרה מבוססת FPGA(תכן, VHDL, סימולציה).
  • רקע בתכן לוגי ובבדיקה של מעגלים ספרתיים.
  • רקע בתכנות בשפת מכונה.
  • תואר שני - יתרון.
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Machine Learning Algorithm Student

Requirements

  • M.Sc. / Ph.D student in Electrical Engineering / Physics / Computer Science / Mathematics.
  • Familiar with Statistics approaches and, Machine Leaning (classical and deep learning) algorithms.
  • Available for at least 22 hours a week
  • At lease one year remaining till graduation
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קניין/ית רכש

דרישות התפקיד :

תואר ראשון במנהל עסקים/תעשיה וניהול/כלכלה -  חובה!

ניסיון בתחום הרכש- חובה!

ניסיון בתחום ברכש חומרי שלד/גמר לבנייה -יתרון משמעותי

יכולת ניהול מו"מ, התנהלות בסביבה מרובת ממשקים ותודעת שירות גבוהה

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