DTU Studieprojekt - Deep learning for electron microscopy - Beam damage

Danmarks Tekniske Universitet (DTU)

Studieprojekt/speciale
Storkøbenhavn

Deep learning for electron microscopy - Beam damage

Udbyder
Vejleder
Sted
København og omegn
In this combined theoretical and experimental project, you will be addressing one of the most pressing problems in modern electron microscopy: the problem of beam damage.
To obtain transmission electron microscopy (TEM) images with atomic resolution, at least 100 high-energy electrons pass through the space occupied by each atom. The energy of these electrons are many orders of magnitude higher than the energy required to break a chemical bond. So it is not surprising that the electron beam influences the sample, both by directly damaging it, and by inducing diffusion and chemical reactions.
Unfortunately, beam damage is poorly understood, both theoretically and phenomoenologically. It is for example not even known when it is the total dose (electrons per area) or the dose rate (electrons per area per time) that is most critical for inducing beam damage. A major problem in studying beam damage it to move from qualitative observations of beam damage, to quantitiative measurements. Here, deep learning comes to the aid.
Deep neural networks allow us to analyze TEM images automatically, identifying where the atoms are, and whether they are moving [1,2]. This motion of the atoms will be influenced by the beam, and by measuring the diffusion as a function of the beam parameters (dose, dose rate and electron energy) we will be able to obtain quantitative measurements of the influence of the beam.
In the project you will be recording image sequences on some of the best transmission electron microscopes in the world, and will be analysing them using modern neural networks. You will be training neural networks suitable for analyzing your own data, and will be writing Python scripts to process the output from the networks and to do the statistical analysis.
Knowledge of Python programming is an advantage, and so is an interest in machine learning / neural networks. Prior knowledge of machine learning is not necessary. You should at least have passed a course in solid state physics (e.g. 10303) OR in electron microscopy (10250).
Figure: A TEM image of a gold nanoparticle on a CeO2 substrate, taken from an image sequence (a TEM video). Analysis of the entire video showing how often the individual surface atoms diffuse. Is this diffusion induced by the electron beam?

References:
[1] J. Madsen et al.: A Deep Learning Approach to Identify Local Structures in Atomic-Resolution Transmission Electron Microscopy Images, Adv. Theory Simul. 1, 1800037 (2018). Preprint available at https://arxiv.org/abs/1802.03008
[2] https://gitlab.com/schiotz/NeuralNetwork_HRTEM
Forudsætninger
Knowledge of solid state physics OR electron microscopy. Some Python programming.

Emneord

Kontakt
Virksomhed/organisation
DTU Fysik

Navn
Jakob Schiøtz

Stilling
Professor

Mail
schiotz@fysik.dtu.dk

Vejleder-info
Kandidatuddannelsen i Geofysik og Rumteknologi
Vejleder
Jakob Schiøtz

Medvejledere
Thomas Willum Hansen, Jakob Birkedal Wagner

ECTS-point
30 - 35

Type
Kandidatspeciale

Kandidatuddannelsen i Fysik og Nanoteknologi
Vejleder
Jakob Schiøtz

Medvejledere
Thomas Willum Hansen, Jakob Birkedal Wagner

ECTS-point
30 - 35

Type
Kandidatspeciale


Løbende ansøgningsfrist

Angiv venligst i din ansøgning, at du har ansøgt opslaget via Studerende Online

Studieprojekt/speciale
Storkøbenhavn
IT
Kemi, Biotek & Materialer
Klima, Miljø & Energi
Matematik, Fysik & Nano
Naturvidenskab
Teknik & Teknologi
Forskning & Udvikling
Naturvidenskab
Teknik



Danmarks Tekniske Universitet (DTU) - hurtigt overblik


Danmarks Tekniske Universitet (DTU)
Danmarks Tekniske Universitet (DTU)
DTU er et teknisk eliteuniversitet med international rækkevidde og standard. Vores mission er at udvikle og nyttiggøre naturvidenskab og teknisk videnskab til gavn for samfundet. 11.200 studerende uddanner sig her til fremtiden, og 6.000 medarbejdere har hver dag fokus på uddannelse, forskning, myndighedsrådgivning og innovation, som bidrager til øget vækst og velfærd.

Placering
Anker Engelunds Vej 1
2800 Kgs. Lyngby
Logo: Danmarks Tekniske Universitet (DTU)
Efterspørgsel efter nye talenter

Hvilke jobtyper og arbejdsområder udbyder vi normalt og hvor mange nye talenter søger vi efter?


Nyeste tweets
Henter tweets...
Facebook feed

Henter facebook feed...

LinkedIn

Følg vores aktiviteter på LinkedIn.


Webside

Få mere info omkring vores virksomhed på vores egne websider:

www.dtu.dk


Danmarks Tekniske Universitet (DTU) i Google

Er der andre informationer om os, som du burde vide? Se, hvad en Google-søgning siger.



https://studerendeonline.dk/job/1242906/
Vi bruger cookies til statistik, sociale medier og brugeroplevelse. Ved at bruge sitet accepterer du vores privatlivspolitik. (luk)
HPT