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

Research Assist­ant - salary grade E 13 TV-L Ber­liner Hoch­schu­len

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

The TU Ber­lin is offer­ing you an excit­ing pos­i­tion as a doc­toral research asso­ci­ate in the pro­ject "Explain­ing 4.0" start­ing Septem­ber 1st, 2020. The pos­i­tion is in the inde­pend­ent research group of Dr. Mar­ina Höhne at the chair for machine learn­ing.

Work­ing field:

Arti­fi­cial intel­li­gence (AI) has achieved aston­ish­ing achieve­ments in a wide vari­ety of areas, such as autonom­ous driv­ing or can­cer cell dia­gnostics. Des­pite, there is a risk espe­cially in secur­ity-crit­ical areas, that decision sys­tems make incor­rect pre­dic­tions. In order to enable a wide use of AI sys­tems, the highly com­plex sys­tems must be bet­ter under­stood. The area of explain­able arti­fi­cial intel­li­gence (Explain­able AI - XAI), which deals with the under­stand­ing of AI mod­els and their decisions, is ground­break­ing here.

The pro­ject “Explain­ing 4.0”, fun­ded by the Fed­eral Min­istry of Edu­ca­tion and Research, aims to develop meth­ods that make a sig­ni­fic­ant con­tri­bu­tion to a hol­istic under­stand­ing of AI mod­els.
For our team we are look­ing for someone who has the goal to do a doc­tor­ate because he*she is enthu­si­astic about sci­ence and is motiv­ated to advance the research field of “explain­able AI” (XAI).

What you can expect:
  • Excit­ing research tasks in the field of machine learn­ing
  • The oppor­tun­ity to make valu­able con­tri­bu­tions to top XAI research
  • Work­ing on pub­lic­a­tions for con­fer­ence and journal papers
  • The oppor­tun­ity to do a doc­tor­ate (PhD)
  • A highly motiv­ated, inter­na­tional team
  • Flex­ible work­ing hours and excel­lent equip­ment.
  • Super­vi­sion by exper­i­enced sci­ent­ists
  • Affil­i­ation to the Chair for Machine Learn­ing at TU Ber­lin
  • Cooper­a­tion with the Ber­lin Insti­tute for the Found­a­tions of Learn­ing and Data (BIFOLD), the Research Group Machine Learn­ing at HHI and the Depart­ment of Com­puter Sci­ence at TU Kais­er­slaut­ern and many oth­ers


  • Suc­cess­fully com­pleted uni­versity degree (Mas­ter, Dip­lom or equi­val­ent) in com­puter sci­ence, phys­ics, engin­eer­ing or applied math­em­at­ics (or sim­ilar)
  • Excel­lent know­ledge in the area of machine learn­ing, espe­cially core meth­ods, deep neural net­works and stat­ist­ical learn­ing the­ory
  • Prac­tical exper­i­ence in the devel­op­ment and applic­a­tion of machine learn­ing algorithms
  • Excel­lent know­ledge in Bayesian learn­ing
  • Exper­i­ence in stat­ist­ical mod­el­ing
  • Know­ledge in game the­ory and lin­ear and non­lin­ear optim­iz­a­tion is be an advant­age
  • Know­ledge in explain­able arti­fi­cial intel­li­gence
  • Pro­found pro­gram­ming know­ledge (Python, C++), espe­cially exper­i­ence with ML and lin­ear algebra lib­rar­ies (PyT­orch, NumPy, sklearn, etc.)
  • Exper­i­ence in using ver­sion con­trol sys­tems, e.g. Git
  • Exper­i­ence with big data pro­gram­ming (e.g. Spark, Hadoop, Flink), par­al­lel pro­gram­ming
  • Exper­i­ence with unix based sys­tems, e.g. Linux
  • Excel­lent writ­ten and spoken Eng­lish skills; good com­mand of Ger­man required; will­ing­ness to learn either Eng­lish or Ger­man is expec­ted.
  • Inde­pend­ent, effect­ive, struc­tured work­ing style
  • Exper­i­ence in inter­dis­cip­lin­ary and cooper­at­ive pro­jects is an advant­age
  • Prac­tical exper­i­ence in pub­lic­a­tions is an advant­age

How to ap­ply:

Please send your applic­a­tion with the ref­er­ence num­ber and the usual doc­u­ments only by email to Dr. Mar­ina Höhne (

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: 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. Mar­ina Höhne, Sekr. MAR 4-1, March­str. 23, 10587 Ber­lin