Blätter-Navigation

An­ge­bot 109 von 610

logo

Tech­ni­sche Uni­ver­sität Ber­lin - Fac­ulty IV - Insti­tute of Soft­ware Engin­eer­ing and The­or­et­ical Com­puter Sci­ence / Maschine Learn­ing

Rese­arch Assist­ant - salary grade E13 TV-L Ber­liner Hoch­schu­len

under the reserve that funds are gran­ted - part-time employ­ment may be pos­sible

The Ber­lin Insti­tute for the Found­a­tions of Learn­ing and Data (BIFOLD) of the TU Ber­lin (Prof. Dr. Klaus-Robert Müller) is look­ing for a research assist­ant for an agil­ity sub-pro­ject (AP) of the BZML, headed by Dr. Gré­go­ire Mon­ta­von.

Work­ing field:

Research in the field of machine learn­ing; devel­op­ment of meth­ods for rep­res­ent­a­tion learn­ing and domain adapt­a­tion, based on the Wasser­stein dis­tance (optimal trans­port). Devel­op­ment of Explain­able AI tech­niques to under­stand the mod­els and their train­ing. Applic­a­tions in the med­ical domain.

Require­ments:

  • Suc­cess­fully com­pleted uni­versity degree (Mas­ter, Dip­lom or equi­val­ent) in Com­puter Sci­ence, Phys­ics, Engin­eer­ing or Applied Math­em­at­ics
  • Pro­found know­ledge in machine learn­ing, in par­tic­u­lar optimal trans­port, rep­res­ent­a­tion learn­ing, neural net­works, explain­able AI
  • Solid pro­gram­ming skills, in par­tic­u­lar exper­i­ence with deep learn­ing frame­works (PyT­orch, Tensor­Flow etc.)
  • Exist­ing pub­lic­a­tions rel­ev­ant to the field is a plus
  • Exper­i­ence with big data and train­ing of ML mod­els on GPUs is a plus
  • Inter­dis­cip­lin­ary and col­lab­or­at­ive pro­ject exper­i­ence is a plus
  • Very good know­ledge of writ­ten and spoken Eng­lish required, know­ledge of Ger­man desir­able or will­ing­ness to learn it

How to ap­ply:

Please send your applic­a­tion with the ref­er­ence num­ber and the usual doc­u­ments (com­bined in a single pdf file, max. 5 MB) by email to Dr. Grégoire Montavon at office@bzml.de.

By sub­mit­ting your applic­a­tion via email you con­sent to hav­ing your data elec­tron­ic­ally pro­cessed and saved. Please note that we do not provide a guar­anty for the pro­tec­tion of your per­sonal data when sub­mit­ted as unpro­tec­ted file. Please find our data pro­tec­tion notice acc. DSGVO (Gen­eral Data Pro­tec­tion Reg­u­la­tion) at the TU staff depart­ment homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/ or quick access 214041.

To ensure equal oppor­tun­it­ies between women and men, applic­a­tions by women with the required qual­i­fic­a­tions are expli­citly desired. Qual­i­fied indi­vidu­als with dis­ab­il­it­ies will be favored. The TU Ber­lin val­ues the diversity of its mem­bers and is com­mit­ted to the goals of equal oppor­tun­it­ies.

Tech­nis­che Uni­versität Ber­lin - Der Präsid­ent - Fak­ultät IV, Insti­tut für Soft­ware­tech­nik und The­or­et­ische Inform­atik, FG Maschinelles Lernen, Dr. Montavon, Sekr. MAR 4-1, March­str. 23, 10587 Ber­lin