ONE POST-DOC POSITION IN THE PROJECT DOMINION
The deadline to apply for this position has expired.
Publication Date
06/06/2019
Application Deadline
20/07/2019
Position Category
Reference Number
2019_9243
Salary
Location
Herakleion, Crete, Greece
Contact Person
Start Date
01/09/2019

Position Description

New deadline for application (ΔΙΔΑΔ/Φ.18.31/2361/οικ.22433)

The successful candidate will study the fundamental physics of iron-based chalcogenide magnets and superconductors, made up of functional molecular components with nanoscale dimensions. For this, user-facility photon and neutron science labs will be utilized to generate key insights on how structure and spontaneous magnetic transitions influence the superconducting electron pairing. Knowledge of correlated-electron physics/materials and physical crystallography, including diffraction experiments and data analysis strategies, will be invaluable for this innovative research project. Young researches will have the opportunity to experience research collaborations with leading research groups based at US DOE labs.

For the full announcement, follow the link ‘Related Documents’

Required Qualifications

  • PhD degree in Physical Sciences
  • Demonstrated ability in quantitative structural analysis methods (e.g. Rietveld, pair distribution function)
  • Proved experience in analytical experimental techniques, including, magnetometry and X-ray diffraction
  • Excellent knowledge of the English language
  • Greek male candidates must have fulfilled their military obligations

Greek male candidates must have fulfilled their military obligations

Desirable Qualifications

  • Publications in peer-reviewed journals and scientific presentations in conferences is desired
  • MSc degree in experimental techniques for condensed matter materials science (e.g. development of low-temperature physics probes) will be an advantage
  • Programming skills for data acquisition and reduction will be beneficial
  • Successful candidates must be able to work in an interdisciplinary environment

Application Procedure

In order to be considered, the application must include:

  • Application Form (download Form, link to the left)
  • A cover letter describing your research interests
  • CV and publications list
  • Two (2) reference letters, e-mailed directly to lappas@iesl.forth.gr and cc to hr@iesl.forth.gr
  • Scanned copies of the ready available academic titles

Appointment Duration

12 months
MACHINE LEARNING FOR COMPLEX SYSTEMS
Event Dates
From: 19/06/2019 10:00
To: 19/06/2019 11:30
External Speaker
George D. Barmparis (Institute of Electronic Structure & Laser, Foundation for Research and Technology-Hellas Heraklion, Crete, Greece and Department of Physics, University of Crete)
Place
FORTH Seminar Room 1

Chimeras and branching are two archetypical complex phenomena that appear in many physical systems; because of their different intrinsic dynamics, they delineate opposite non-trivial limits in the complexity of wave motion and present severe challenges in predicting chaotic and singular behavior in extended physical systems. We report on the long-term forecasting capability of Long Short-Term Memory (LSTM) and reservoir computing (RC) recurrent neural networks, when they are applied to the spatiotemporal evolution of turbulent chimeras in simulated arrays of coupled superconducting quantum interference devices (SQUIDs) or lasers, and branching in the electronic flow of two-dimensional graphene with random potential. We propose a new method in which we assign one LSTM network to each system node except for “observer” nodes which provide continual “ground truth” measurements as input; we refer to this method as “Observer LSTM” (OLSTM). We demonstrate that even a small number of observers greatly improves the data-driven (model-free) long-term forecasting capability of the LSTM networks and provide the framework for a consistent comparison between the RC and LSTM methods. We find that RC requires smaller training datasets than OLSTMs, but the latter require fewer observers. Both methods are benchmarked against Feed-Forward neural networks (FNNs), also trained to make predictions with observers (OFNNs). Extensions of this method are applied in other dynamical systems.

ONE (1) TECHNICIAN POSITION IN THE PROJECT NANOSMART
The deadline to apply for this position has expired.
Publication Date
30/05/2019
Application Deadline
13/07/2019
Position Category
Reference Number
2019_8933
Salary
Location
Herakleion, Crete, Greece
Contact Person
Start Date
01/08/2019

Position Description

New deadline for application (ΔΙΔΑΔ/Φ.18.31/2361/οικ.22433)

One Technician position for design, static and dynamic simulation using finite element method (using TCAd Silvaco) on field effect transistors (FET). Measurement of electrical characteristics and extraction of parameters for custom models.

For the full announcement, follow the link ‘Related Documents’

Related Project

NANOSMART -

Required Qualifications

  • Basic degree in Electrical or Electronic engineering (30%)
  • Postgraduate studies in Electronics engineering (25%)
  • Minimum of 3 years experience in research (15%)
  • Experience in using Silvaco TCAD Athena and Atlas circuit and device simulators (15%)

Desirable Qualifications

  • Experience in technical support of research lab (5%)
  • Prior experience in verilog-A (5%)
  • Knowledge of software development (in programming languages such as Python, Java) for collection and analysis of measured data (5%)

Application Procedure

In order to be considered, the application must include:
- Completed application Form (Download link to the left)
- Brief CV
- Scanned copies of academic titles
- Reference letters (if required)
- All required forms and documents as layed out in each Job opening description

Please send your application and all documents to: hr@iesl.forth.gr and cc the Scientific supervisor marked in the left column

Appointment Duration

5 months

Funding

Pages